{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kaggle - Web Traffic Time Series Forecasting\n",
    "\n",
    "# Modified CNN with Fibonacci Median\n",
    "\n",
    "**v10.7 Grouping, Ensemble, and Optimized Group Models**\n",
    "\n",
    "by Louis Yang and Sky Huang, 2017\n",
    "\n",
    "#### Model Explanation\n",
    "We use a Fibonacci series as the window sizes to compute the a serie of median excluding nan for each sample (page). Then find again the median amount the series excluding nan. Use this median-median (which we call Fibonacci median) as the center of each sample. The Fibonaccis series we took is `[11, 18, 30, 48, 78, 126, 203, 329]`, which was suggested by Ehsan  http://www.kaggle.com/safavieh/median-estimation-by-fibonacci-et-al-lb-44-9?scriptVersionId=1466647 and has original scored 44.9 in the stage 1 Kaggle leaderboard.\n",
    "\n",
    "To better train the neural network for various scales of dayily visits, we first do the log1p transformation in the form \n",
    "    \n",
    "    X_log = log10(X_ori + 1),\n",
    "\n",
    "to bring them into the same order. Then we sample-wise (page-wise) standardize the data using the Fibonacci median (`fib_med`) instead of regular mean as the center baseline and the usual standard deviation (`stdev`) as the scale, where `nan` is treated as `0`.\n",
    "\n",
    "According to the Fibonacci median (`fib_med`), we split data (pages) into groups and train individual neural network (models) in each group. The group spliting is determined by `log10(fib_med + 1) < (1.0, 2.0, 4.0, greater)`. So there are total 4 groups.\n",
    "\n",
    "The first group (group 0) use the result from the Fibonacci median model (`fib_med`) since it is difficult to learn by our neural network. For the rest groups (group 1-3), we use the results from the convolutional neural network (CNN). \n",
    "\n",
    "The neural network takes 64 days (`x_length`) of data and predicts the following 64 days (`y_length`) of results. For the neural network structure, we use single 1D convolutional layer with 140 neurons, kernel size 3, and relu activation function, which pass through average pooling with size 2. After the flatten the convolutional result, we feed the Fibonacci median and stdev (after log transform) for the sample as addional inputs via concatenation. Finally, the concatenated data is pass into 3 fully connected layer (130, 120, and 64 neurons) with 2 relu and 1 linear activation functions to do the regression. \n",
    "\n",
    "The CNN structure is: \n",
    "\n",
    "    [X > Conv1D(140, kernel=3) > AvgPool(2) > Flat + Additional input, A, (median, stdev)] > Concat > FC(130, relu) > FC(120, relu) > FC(64, linear) > Y\n",
    "\n",
    "Ensemble learning: We train the same neural network 5 times. Each run only train on 4/5 of the data. Then we take the median of the result from each run.\n",
    "\n",
    "Group model optimization: We evaluate the group models (neural network trained within their own group) on other groups data, and assign their results based on their performance in test mode. Since in the test (validation) mode the neural network model trained by group 2 do better than those trained by group 3 on group 3 data, we use the group model 2 for predicting group 3 result.\n",
    "\n",
    "#### Usage\n",
    "This notebook has two modes (controled by `test_length`): the test mode (`test_length != 0`) and the predicting mode (test_length = 0).\n",
    "\n",
    "The default parameter of this notebook is in the predicting mode. In predicting mode, it will train on stage 2 data `'train_3.csv'` (the latest `'train_2.csv'` on Kaggle website) and output prediction required by the stage 2 of the competition. In this mode, the SMAPE scores are computed on the input set as estimation. To switch to test mode (for model self-evaluation, validation, and parameter tunning purpose), you can set the `test_length` to the lengh (days) of the ouput prediction (ex. `test_length = y_length`).\n",
    "\n",
    "Required input files: `'train_3.csv'`, `'key_2.csv'`\n",
    "\n",
    "#### Statistics\n",
    "Runtime: 1hr20min on Intel Core i7-4700MQ\n",
    "\n",
    "Peak memory usage: 7+ GB (durning test and training) and 9+ GB (durning output prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_name ='model_modified_CNN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import gc  # Python garbage collect\n",
    "from tools import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_file = 'train_3.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "key_file = 'key_2.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-04', '2015-07-05',\n",
       "       '2015-07-06', '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10',\n",
       "       ...\n",
       "       '2017-09-01', '2017-09-02', '2017-09-03', '2017-09-04', '2017-09-05',\n",
       "       '2017-09-06', '2017-09-07', '2017-09-08', '2017-09-09', '2017-09-10'],\n",
       "      dtype='object', length=803)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv('../data/' + train_file, nrows=1)\n",
    "input_df_dates = input_df.columns[1:]; input_df_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%%% Reading data train_3.csv ... done!\n"
     ]
    }
   ],
   "source": [
    "visits_dtype = {d: np.float32 for d in input_df_dates}\n",
    "\n",
    "print('%%% Reading data '+ train_file + ' ... ', end = '', flush = True)\n",
    "input_df = pd.read_csv('../data/' + train_file, engine='c', dtype=visits_dtype)\n",
    "print('done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page</th>\n",
       "      <th>2015-07-01</th>\n",
       "      <th>2015-07-02</th>\n",
       "      <th>2015-07-03</th>\n",
       "      <th>2015-07-04</th>\n",
       "      <th>2015-07-05</th>\n",
       "      <th>2015-07-06</th>\n",
       "      <th>2015-07-07</th>\n",
       "      <th>2015-07-08</th>\n",
       "      <th>2015-07-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-09-01</th>\n",
       "      <th>2017-09-02</th>\n",
       "      <th>2017-09-03</th>\n",
       "      <th>2017-09-04</th>\n",
       "      <th>2017-09-05</th>\n",
       "      <th>2017-09-06</th>\n",
       "      <th>2017-09-07</th>\n",
       "      <th>2017-09-08</th>\n",
       "      <th>2017-09-09</th>\n",
       "      <th>2017-09-10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2NE1_zh.wikipedia.org_all-access_spider</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2PM_zh.wikipedia.org_all-access_spider</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3C_zh.wikipedia.org_all-access_spider</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4minute_zh.wikipedia.org_all-access_spider</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 804 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Page  2015-07-01  2015-07-02  \\\n",
       "0            2NE1_zh.wikipedia.org_all-access_spider        18.0        11.0   \n",
       "1             2PM_zh.wikipedia.org_all-access_spider        11.0        14.0   \n",
       "2              3C_zh.wikipedia.org_all-access_spider         1.0         0.0   \n",
       "3         4minute_zh.wikipedia.org_all-access_spider        35.0        13.0   \n",
       "4  52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...         NaN         NaN   \n",
       "\n",
       "   2015-07-03  2015-07-04  2015-07-05  2015-07-06  2015-07-07  2015-07-08  \\\n",
       "0         5.0        13.0        14.0         9.0         9.0        22.0   \n",
       "1        15.0        18.0        11.0        13.0        22.0        11.0   \n",
       "2         1.0         1.0         0.0         4.0         0.0         3.0   \n",
       "3        10.0        94.0         4.0        26.0        14.0         9.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   2015-07-09     ...      2017-09-01  2017-09-02  2017-09-03  2017-09-04  \\\n",
       "0        26.0     ...            19.0        33.0        33.0        18.0   \n",
       "1        10.0     ...            32.0        30.0        11.0        19.0   \n",
       "2         4.0     ...             6.0         6.0         7.0         2.0   \n",
       "3        11.0     ...             7.0        19.0        19.0         9.0   \n",
       "4         NaN     ...            16.0        16.0        19.0         9.0   \n",
       "\n",
       "   2017-09-05  2017-09-06  2017-09-07  2017-09-08  2017-09-09  2017-09-10  \n",
       "0        16.0        27.0        29.0        23.0        54.0        38.0  \n",
       "1        54.0        25.0        26.0        23.0        13.0        81.0  \n",
       "2         4.0         7.0         3.0         4.0         7.0         6.0  \n",
       "3         6.0        16.0        19.0        30.0        38.0         4.0  \n",
       "4        20.0        23.0        28.0        14.0         8.0         7.0  \n",
       "\n",
       "[5 rows x 804 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Data\n",
    "Generate training set and test (predict) set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Day of week alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "def weekday(datestr):\n",
    "    return datetime.strptime(datestr,'%Y-%m-%d').weekday()\n",
    "def daydiff(dstr1,dstr2):\n",
    "    return datetime.strptime(dstr1,'%Y-%m-%d') - datetime.strptime(dstr2,'%Y-%m-%d') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to predict output (Y_output_pred) start from Sunday (6) for data set 1. (0: Monday, 6: Sunday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the x_length + y_length days of data only.\n",
    "\n",
    "This part then split into x_length as input X and y_length as output Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_length = 64  # input period\n",
    "y_length = 64  # predict period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_length = 0  # for predicting\n",
    "#test_length = y_length  # for self testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_dates = input_df_dates[-x_length-test_length-364:-test_length-364]\n",
    "\n",
    "if test_length:\n",
    "    X_output_dates = input_df_dates[-x_length-test_length:-test_length]\n",
    "else:\n",
    "    X_output_dates = input_df_dates[-x_length:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_input_first_day:  2016-07-10 6\n",
      "X_input_final_day:  2016-09-11 6\n",
      "X_input_days_diff:  63 days, 0:00:00\n",
      "X_output_first_day: 2017-07-09 6\n",
      "X_output_final_day: 2017-09-10 6\n",
      "X_output_days_diff: 63 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "print('X_input_first_day: ', X_input_dates[0], weekday(X_input_dates[0]))\n",
    "print('X_input_final_day: ', X_input_dates[-1], weekday(X_input_dates[-1]))\n",
    "print('X_input_days_diff: ', daydiff(X_input_dates[-1], X_input_dates[0]))\n",
    "\n",
    "print('X_output_first_day:', X_output_dates[0], weekday(X_output_dates[0]))\n",
    "print('X_output_final_day:', X_output_dates[-1], weekday(X_output_dates[-1]))\n",
    "print('X_output_days_diff:', daydiff(X_output_dates[-1], X_output_dates[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submission required days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Page            Id        date\n",
      "0  007_スペクター_ja.wikipedia.org_all-access_all-agents  0b293039387a  2017-09-13\n",
      "1  007_スペクター_ja.wikipedia.org_all-access_all-agents  7114389dd824  2017-09-14\n",
      "2  007_スペクター_ja.wikipedia.org_all-access_all-agents  057b02ff1f09  2017-09-15\n",
      "3  007_スペクター_ja.wikipedia.org_all-access_all-agents  bd2aca21caa3  2017-09-16\n",
      "4  007_スペクター_ja.wikipedia.org_all-access_all-agents  c0effb42cdd5  2017-09-17\n"
     ]
    }
   ],
   "source": [
    "if test_length == 0:  # Prefetch the key_file\n",
    "    output_df = pd.read_csv('../data/' + key_file, nrows=100)    # Read the first 100 days of required submission\n",
    "    output_df['date'] = output_df.Page.apply(lambda a: a[-10:])  # take the last 10 characters from 'Page' as date\n",
    "    output_df['Page'] = output_df.Page.apply(lambda a: a[:-11])  # remove the last 10 caharacters from 'Page'\n",
    "    print(output_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "submission required first day: 2017-09-13\n",
      "submission required final day: 2017-11-13\n"
     ]
    }
   ],
   "source": [
    "if test_length == 0:\n",
    "    output_df_dates_all = output_df.date.values.astype('datetime64[D]')\n",
    "    output_df_first_day = str(output_df_dates_all.min())\n",
    "    output_df_final_day = str(output_df_dates_all.max())\n",
    "    print('submission required first day:', output_df_first_day)\n",
    "    print('submission required final day:', output_df_final_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_input_dates = input_df_dates[-test_length-364:-test_length-364+y_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Y for output set\n",
    "if test_length:\n",
    "    Y_output_dates = input_df_dates[-test_length:]\n",
    "else:\n",
    "    Y_output_first_day = '2017-09-11'  ## Make sure it is correct when predicting !!!!!!!\n",
    "    Y_output_dates = pd.Index(np.arange(np.datetime64(Y_output_first_day), \n",
    "                                        np.datetime64(Y_output_first_day)\n",
    "                                        + np.timedelta64(y_length, 'D')).astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y_input_first_day:  2016-09-12 0\n",
      "Y_input_final_day:  2016-11-14 0\n",
      "Y_input_days_diff:  63 days, 0:00:00\n",
      "Y_output_first_day: 2017-09-11 0\n",
      "Y_output_final_day: 2017-11-13 0\n",
      "Y_output_days_diff: 63 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "print('Y_input_first_day: ', Y_input_dates[0], weekday(Y_input_dates[0]))\n",
    "print('Y_input_final_day: ', Y_input_dates[-1], weekday(Y_input_dates[-1]))\n",
    "print('Y_input_days_diff: ', daydiff(Y_input_dates[-1], Y_input_dates[0]))\n",
    "\n",
    "print('Y_output_first_day:', Y_output_dates[0], weekday(Y_output_dates[0]))\n",
    "print('Y_output_final_day:', Y_output_dates[-1], weekday(Y_output_dates[-1]))\n",
    "print('Y_output_days_diff:', daydiff(Y_output_dates[-1], Y_output_dates[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fibonacci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Window Sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The window sizes are given by Ehsan https://www.kaggle.com/safavieh/median-estimation-by-fibonacci-et-al-lb-44-9?scriptVersionId=1466647"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "windows = [11, 18, 30, 48, 78, 126, 203, 329]\n",
    "fib_length = max(windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this testing code, we did not include the last y_length days since in the real predicting stage, we don't know their visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if test_length:\n",
    "    fib_output_dates = input_df_dates[-fib_length-test_length:-test_length]\n",
    "else:\n",
    "    fib_output_dates = input_df_dates[-fib_length:]\n",
    "fib_input_dates = input_df_dates[-fib_length-test_length-364:-test_length-364]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fib_output_data = input_df[fib_output_dates].values\n",
    "fib_input_data = input_df[fib_input_dates].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fibonacci median in each window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\numpy\\lib\\function_base.py:4011: RuntimeWarning: All-NaN slice encountered\n",
      "  r = func(a, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "fib_input_median_list = np.array([np.nanmedian(fib_input_data[:, -w:] , axis=-1) \n",
    "                                  for w in windows])\n",
    "fib_output_median_list = np.array([np.nanmedian(fib_output_data[:, -w:] , axis=-1) \n",
    "                                   for w in windows])\n",
    "# Runtime: 22 sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\numpy\\lib\\function_base.py:4011: RuntimeWarning: All-NaN slice encountered\n",
      "  r = func(a, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "fib_input_median = np.nan_to_num(np.nanmedian(fib_input_median_list.T, axis=-1))\n",
    "fib_output_median = np.nan_to_num(np.nanmedian(fib_output_median_list.T, axis=-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "349"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del fib_output_data\n",
    "del fib_input_data\n",
    "del fib_output_median_list\n",
    "del fib_input_median_list\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this as the center and part of the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_input_fib = fib_input_median.reshape(-1,1)\n",
    "Y_output_fib = fib_output_median.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Selection\n",
    "Select x_length + y_length days of data as input or ouput set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input and Output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_raw = input_df[X_input_dates].values\n",
    "Y_input_raw = input_df[Y_input_dates].values\n",
    "X_output_raw = input_df[X_output_dates].values\n",
    "if test_length:\n",
    "    Y_output_raw = input_df[Y_output_dates].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Convert nan to number 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_num = np.nan_to_num(X_input_raw)\n",
    "Y_input_num = np.nan_to_num(Y_input_raw)\n",
    "X_output_num = np.nan_to_num(X_output_raw)\n",
    "if test_length:\n",
    "    Y_output_num = np.nan_to_num(Y_output_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def log(X):\n",
    "    return np.log10(X + 1.0)\n",
    "def unlog(X):\n",
    "    return np.clip(np.power(10., X) - 1.0, 0.0, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_raw_log = log(X_input_raw)  # Contain nan\n",
    "Y_input_raw_log = log(Y_input_raw)\n",
    "X_output_raw_log = log(X_output_raw)\n",
    "if test_length:\n",
    "    Y_output_raw_log = log(Y_output_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_log = log(X_input_num)  # Do not contain nan\n",
    "Y_input_log = log(Y_input_num)\n",
    "X_output_log = log(X_output_num)\n",
    "if test_length:\n",
    "    Y_output_log = log(Y_output_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization (Center and Scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output_center = log(fib_output_median).reshape(-1,1) # Fib. median as the center\n",
    "input_center = log(fib_input_median).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_center = input_center # Use same center for X and Y\n",
    "Y_input_center = input_center\n",
    "X_output_center = output_center\n",
    "Y_output_center = output_center"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choice of default scale for NaN data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default_scale: 0.174351\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\numpy\\lib\\nanfunctions.py:1427: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  keepdims=keepdims)\n"
     ]
    }
   ],
   "source": [
    "default_input_scale = np.nanmedian(np.nanstd(X_input_raw_log, axis=-1))  # Do not include nan\n",
    "default_output_scale = np.nanmedian(np.nanstd(X_output_raw_log, axis=-1))  # Do not include nan\n",
    "default_scale = np.mean([default_input_scale, default_output_scale])\n",
    "print('default_scale:', default_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Treat nan as 0\n",
    "input_scale = np.std(X_input_log, axis=-1).reshape(-1,1)\n",
    "output_scale = np.std(X_output_log, axis=-1).reshape(-1,1)\n",
    "\n",
    "input_scale[input_scale == 0.0] = default_scale  # Prevent divid by zero \n",
    "output_scale[output_scale == 0.0] = default_scale  # Prevent divid by zero "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_scale = input_scale\n",
    "Y_input_scale = input_scale\n",
    "X_output_scale = output_scale\n",
    "Y_output_scale = output_scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shift by Center and Scale by Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform(data_ori, center, scale):\n",
    "    return (data_ori - center) / scale\n",
    "def untransform(data, center, scale):\n",
    "    return data * scale + center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Normalize before nan->0\n",
    "X_input = np.nan_to_num(transform(X_input_raw_log, X_input_center, input_scale))\n",
    "X_output = np.nan_to_num(transform(X_output_raw_log, X_output_center, output_scale))\n",
    "Y_input = np.nan_to_num(transform(Y_input_raw_log, Y_input_center, input_scale))\n",
    "if test_length:\n",
    "    Y_output = np.nan_to_num(transform(Y_output_raw_log, Y_output_center, output_scale))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:\n",
    "\n",
    "* If we take nan_to_num before transform, we make sure the nan got transformed into 0 but not the center.\n",
    "* If we take nan_to_num after transform, nan trun into the center but not 0 of the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check median and stdev transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_nan(X):\n",
    "    return [x for x in X if np.isnan(x).any()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group index based on scale of median (center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_index(logx):\n",
    "    if logx < 1.0: return 0\n",
    "    elif logx < 2.0: return 1\n",
    "    elif logx < 4.0: return 2\n",
    "    else: return 3\n",
    "group_index_v = np.vectorize(group_index)\n",
    "gp_list = list(range(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group using Y center (Fib Median)\n",
    "input_gp = group_index_v(Y_input_center).reshape(-1)\n",
    "output_gp = group_index_v(Y_output_center).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gp_input_counts: [27413, 40041, 76815, 794]\n"
     ]
    }
   ],
   "source": [
    "# group counts\n",
    "gp_input_counts = [0] * len(gp_list)\n",
    "for x in input_gp: gp_input_counts[x] += 1\n",
    "print('gp_input_counts:', gp_input_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gp_output_counts: [25996, 43804, 74588, 675]\n"
     ]
    }
   ],
   "source": [
    "# group counts\n",
    "gp_output_counts = [0] * len(gp_list)\n",
    "for x in output_gp: gp_output_counts[x] += 1\n",
    "print('gp_output_counts:', gp_output_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_ori = X_input_num  # Do not contain nan\n",
    "Y_input_ori = Y_input_num\n",
    "X_output_ori = X_output_num\n",
    "if test_length:\n",
    "    Y_output_ori = Y_output_num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split X,Y data into groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_input_list = [X_input[input_gp == gp] for gp in gp_list]\n",
    "Y_input_list = [Y_input[input_gp == gp] for gp in gp_list]\n",
    "X_output_list = [X_output[output_gp == gp] for gp in gp_list]\n",
    "if test_length:\n",
    "    Y_output_list = [Y_output[output_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional input for the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_input = np.concatenate((Y_input_center, input_scale), axis=1)\n",
    "A_output = np.concatenate((Y_output_center, output_scale), axis=1)\n",
    "\n",
    "A_input_list = [A_input[input_gp == gp] for gp in gp_list]\n",
    "A_output_list = [A_output[output_gp == gp] for gp in gp_list]\n",
    "\n",
    "a_length = len(A_input[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dates that We don't evaluate their scores\n",
    "We modify Y_output_raw so that some of the dates won't be evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of day we don't evaluate: 1\n"
     ]
    }
   ],
   "source": [
    "y_eval_length = 63\n",
    "y_not_eval = y_length - y_eval_length  # number of days we don't evaluate their scores\n",
    "print(\"Number of day we don't evaluate:\", y_not_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length and y_not_eval > 0:\n",
    "    Y_output_raw[:,:y_not_eval] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length and y_not_eval > 0:\n",
    "    print(np.sum(~np.isnan(Y_output_raw[0])), y_eval_length, y_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length and y_not_eval > 0:\n",
    "    print(Y_output_raw[0:2,y_not_eval-1:y_not_eval+1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shuffle Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_shuffle_index(n, seed=None):\n",
    "    shuffle_index = np.arange(n)\n",
    "    np.random.seed(seed)  # you can fix the initial seed for comparison purpose\n",
    "    np.random.shuffle(shuffle_index)\n",
    "    return shuffle_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMAPE\n",
    "Symmetric mean absolute percentage error\n",
    "\n",
    "https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "def k_smape(y_true, y_pred):\n",
    "    '''Symmetric mean absolute percentage error for keras metric'''\n",
    "    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true) + K.abs(y_pred),\n",
    "                                            K.epsilon(), None))\n",
    "    return 200. * K.mean(diff, axis=-1)\n",
    "\n",
    "def smape(y_true, y_pred, axis=None):\n",
    "    '''Symmetric mean absolute percentage error'''\n",
    "    diff = np.abs((y_true - y_pred) / \n",
    "                  np.clip(np.abs(y_true) + np.abs(y_pred), np.float32(1e-07), None))\n",
    "    return np.float32(200.) * np.nanmean(diff, axis=axis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network Model\n",
    "With the flavor of convolutional neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Model, load_model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv1D\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import MaxPooling1D\n",
    "from keras.layers import AveragePooling1D\n",
    "from keras.layers import concatenate\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_ens = 5  # Number of ensembles\n",
    "ens_list = list(range(n_ens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not loading.\n"
     ]
    }
   ],
   "source": [
    "#load exisiting model\n",
    "load_previous_models = False  # Switch for determine whether to load the exisiting model or not\n",
    "if load_previous_models:\n",
    "    print('Load Previous Models')\n",
    "    models_ens = [[load_model('../models/' + model_name + '-' + str(run) + '-' + str(gp) +'.h5',\n",
    "                              custom_objects={'k_smape': k_smape})\n",
    "                   for gp in gp_list]\n",
    "                  for run in ens_list]\n",
    "else: print('Not loading.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 0: Group-0 Group-1 Group-2 Group-3 \n",
      "Run 1: Group-0 Group-1 Group-2 Group-3 \n",
      "Run 2: Group-0 Group-1 Group-2 Group-3 \n",
      "Run 3: Group-0 Group-1 Group-2 Group-3 \n",
      "Run 4: Group-0 Group-1 Group-2 Group-3 \n"
     ]
    }
   ],
   "source": [
    "if not load_previous_models:\n",
    "    models_ens = []\n",
    "    for run in ens_list:\n",
    "        print('Run', run, end=': ')\n",
    "        models = []\n",
    "        for gp in gp_list:\n",
    "            print('Group-', gp, sep='', end=' ')\n",
    "            layer_0 = Input(shape=(x_length,), name='x_input')\n",
    "            layer_t = Reshape((-1, 1))(layer_0)\n",
    "            layer_t = Conv1D(140, kernel_size=3, activation='relu')(layer_t)\n",
    "            layer_t = AveragePooling1D(pool_size=2)(layer_t)\n",
    "            layer_cnn_x = Flatten()(layer_t)\n",
    "\n",
    "            layer_a = Input(shape=(a_length,), name='a_input')\n",
    "\n",
    "            layer_t = concatenate([layer_cnn_x, layer_a])\n",
    "\n",
    "            layer_t = Dense(130, activation='relu')(layer_t)\n",
    "            layer_t = Dropout(0.25)(layer_t)\n",
    "            layer_t = Dense(120, activation='relu')(layer_t)\n",
    "            layer_t = Dropout(0.5)(layer_t)\n",
    "            layer_f = Dense(y_length)(layer_t)\n",
    "\n",
    "            model = Model(inputs=[layer_0, layer_a], outputs=layer_f)\n",
    "            model.compile(optimizer='adam',\n",
    "                          loss='mean_absolute_error', metrics=[k_smape])\n",
    "            models.append(model)\n",
    "        models_ens.append(models)\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "x_input (InputLayer)             (None, 64)            0                                            \n",
      "____________________________________________________________________________________________________\n",
      "reshape_20 (Reshape)             (None, 64, 1)         0           x_input[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "conv1d_20 (Conv1D)               (None, 62, 140)       560         reshape_20[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling1d_20 (AveragePoo (None, 31, 140)       0           conv1d_20[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "flatten_20 (Flatten)             (None, 4340)          0           average_pooling1d_20[0][0]       \n",
      "____________________________________________________________________________________________________\n",
      "a_input (InputLayer)             (None, 2)             0                                            \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_20 (Concatenate)     (None, 4342)          0           flatten_20[0][0]                 \n",
      "                                                                   a_input[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_58 (Dense)                 (None, 130)           564590      concatenate_20[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "dropout_39 (Dropout)             (None, 130)           0           dense_58[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_59 (Dense)                 (None, 120)           15720       dropout_39[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "dropout_40 (Dropout)             (None, 120)           0           dense_59[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_60 (Dense)                 (None, 64)            7744        dropout_40[0][0]                 \n",
      "====================================================================================================\n",
      "Total params: 588,614\n",
      "Trainable params: 588,614\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "models_ens[-1][-1].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N of ensemble, N of groups: (5, 4)\n"
     ]
    }
   ],
   "source": [
    "print('N of ensemble, N of groups:', np.array(models_ens).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs_list = [0,40,40,60]  # Epochs setting for each group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train neural networks\n",
    "Takes a few hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Run: 1 / 5 ===\n",
      "--- Group: 1 / 4 ---\n",
      "Train on 21930 samples, validate on 5483 samples\n",
      "--- Group: 2 / 4 ---\n",
      "Train on 32032 samples, validate on 8009 samples\n",
      "Epoch 1/40\n",
      "9s - loss: 1.0614 - k_smape: 156.7548 - val_loss: 1.0359 - val_k_smape: 150.0568\n",
      "Epoch 2/40\n",
      "7s - loss: 1.0396 - k_smape: 148.7718 - val_loss: 1.0283 - val_k_smape: 146.3537\n",
      "Epoch 3/40\n",
      "7s - loss: 1.0338 - k_smape: 146.9182 - val_loss: 1.0255 - val_k_smape: 147.0556\n",
      "Epoch 4/40\n",
      "7s - loss: 1.0285 - k_smape: 145.4173 - val_loss: 1.0254 - val_k_smape: 148.6360\n",
      "Epoch 5/40\n",
      "7s - loss: 1.0245 - k_smape: 144.4631 - val_loss: 1.0209 - val_k_smape: 143.8284\n",
      "Epoch 6/40\n",
      "7s - loss: 1.0214 - k_smape: 143.8143 - val_loss: 1.0214 - val_k_smape: 142.9885\n",
      "Epoch 7/40\n",
      "7s - loss: 1.0179 - k_smape: 143.1638 - val_loss: 1.0169 - val_k_smape: 141.3128\n",
      "Epoch 8/40\n",
      "7s - loss: 1.0143 - k_smape: 142.5879 - val_loss: 1.0154 - val_k_smape: 144.7105\n",
      "Epoch 9/40\n",
      "7s - loss: 1.0118 - k_smape: 142.0580 - val_loss: 1.0137 - val_k_smape: 141.8030\n",
      "Epoch 10/40\n",
      "7s - loss: 1.0092 - k_smape: 141.6441 - val_loss: 1.0148 - val_k_smape: 142.8304\n",
      "Epoch 11/40\n",
      "7s - loss: 1.0070 - k_smape: 141.0235 - val_loss: 1.0116 - val_k_smape: 141.9576\n",
      "Epoch 12/40\n",
      "7s - loss: 1.0050 - k_smape: 140.9724 - val_loss: 1.0141 - val_k_smape: 139.6199\n",
      "Epoch 13/40\n",
      "7s - loss: 1.0019 - k_smape: 140.2178 - val_loss: 1.0149 - val_k_smape: 144.2058\n",
      "Epoch 14/40\n",
      "7s - loss: 0.9997 - k_smape: 139.8449 - val_loss: 1.0122 - val_k_smape: 141.3872\n",
      "Epoch 15/40\n",
      "7s - loss: 0.9969 - k_smape: 139.6378 - val_loss: 1.0133 - val_k_smape: 143.1630\n",
      "Epoch 16/40\n",
      "7s - loss: 0.9947 - k_smape: 139.5659 - val_loss: 1.0129 - val_k_smape: 142.3068\n",
      "Epoch 17/40\n",
      "7s - loss: 0.9923 - k_smape: 139.4299 - val_loss: 1.0096 - val_k_smape: 141.4651\n",
      "Epoch 18/40\n",
      "7s - loss: 0.9900 - k_smape: 138.9220 - val_loss: 1.0137 - val_k_smape: 143.4455\n",
      "Epoch 19/40\n",
      "7s - loss: 0.9868 - k_smape: 138.3725 - val_loss: 1.0100 - val_k_smape: 142.0482\n",
      "Epoch 20/40\n",
      "7s - loss: 0.9862 - k_smape: 138.5253 - val_loss: 1.0130 - val_k_smape: 142.7213\n",
      "Epoch 21/40\n",
      "7s - loss: 0.9820 - k_smape: 138.0303 - val_loss: 1.0122 - val_k_smape: 142.0677\n",
      "Epoch 22/40\n",
      "7s - loss: 0.9803 - k_smape: 138.0771 - val_loss: 1.0139 - val_k_smape: 140.3011\n",
      "Epoch 23/40\n",
      "7s - loss: 0.9795 - k_smape: 137.7921 - val_loss: 1.0139 - val_k_smape: 143.3851\n",
      "Epoch 24/40\n",
      "7s - loss: 0.9785 - k_smape: 137.6687 - val_loss: 1.0144 - val_k_smape: 143.0590\n",
      "Epoch 25/40\n",
      "7s - loss: 0.9755 - k_smape: 137.3873 - val_loss: 1.0147 - val_k_smape: 142.7718\n",
      "Epoch 26/40\n",
      "7s - loss: 0.9740 - k_smape: 137.1587 - val_loss: 1.0120 - val_k_smape: 140.6082\n",
      "Epoch 27/40\n",
      "7s - loss: 0.9733 - k_smape: 137.0779 - val_loss: 1.0138 - val_k_smape: 139.6278\n",
      "Epoch 28/40\n",
      "7s - loss: 0.9696 - k_smape: 136.7369 - val_loss: 1.0183 - val_k_smape: 140.3130\n",
      "Epoch 29/40\n",
      "7s - loss: 0.9681 - k_smape: 136.8629 - val_loss: 1.0128 - val_k_smape: 140.1762\n",
      "Epoch 30/40\n",
      "7s - loss: 0.9663 - k_smape: 136.4300 - val_loss: 1.0149 - val_k_smape: 140.7939\n",
      "Epoch 31/40\n",
      "7s - loss: 0.9669 - k_smape: 136.5504 - val_loss: 1.0156 - val_k_smape: 140.3555\n",
      "Epoch 32/40\n",
      "7s - loss: 0.9637 - k_smape: 136.0417 - val_loss: 1.0159 - val_k_smape: 142.6991\n",
      "Epoch 33/40\n",
      "7s - loss: 0.9627 - k_smape: 136.0685 - val_loss: 1.0140 - val_k_smape: 141.2193\n",
      "Epoch 34/40\n",
      "7s - loss: 0.9616 - k_smape: 135.9119 - val_loss: 1.0131 - val_k_smape: 141.4145\n",
      "Epoch 35/40\n",
      "7s - loss: 0.9602 - k_smape: 135.9412 - val_loss: 1.0153 - val_k_smape: 140.2137\n",
      "Epoch 36/40\n",
      "7s - loss: 0.9579 - k_smape: 135.6847 - val_loss: 1.0140 - val_k_smape: 141.3542\n",
      "Epoch 37/40\n",
      "7s - loss: 0.9571 - k_smape: 135.6660 - val_loss: 1.0152 - val_k_smape: 141.3418\n",
      "Epoch 38/40\n",
      "7s - loss: 0.9567 - k_smape: 135.7214 - val_loss: 1.0150 - val_k_smape: 141.1108\n",
      "Epoch 39/40\n",
      "7s - loss: 0.9543 - k_smape: 135.1920 - val_loss: 1.0157 - val_k_smape: 141.4405\n",
      "Epoch 40/40\n",
      "7s - loss: 0.9520 - k_smape: 135.0336 - val_loss: 1.0151 - val_k_smape: 141.4559\n",
      "--- Group: 3 / 4 ---\n",
      "Train on 61452 samples, validate on 15363 samples\n",
      "Epoch 1/40\n",
      "14s - loss: 1.0898 - k_smape: 128.3304 - val_loss: 1.0318 - val_k_smape: 118.4173\n",
      "Epoch 2/40\n",
      "13s - loss: 1.0479 - k_smape: 120.8273 - val_loss: 1.0203 - val_k_smape: 119.6346\n",
      "Epoch 3/40\n",
      "13s - loss: 1.0359 - k_smape: 119.0996 - val_loss: 1.0220 - val_k_smape: 120.4505\n",
      "Epoch 4/40\n",
      "13s - loss: 1.0284 - k_smape: 117.9683 - val_loss: 1.0054 - val_k_smape: 116.5778\n",
      "Epoch 5/40\n",
      "13s - loss: 1.0207 - k_smape: 116.9440 - val_loss: 0.9994 - val_k_smape: 115.9935\n",
      "Epoch 6/40\n",
      "13s - loss: 1.0150 - k_smape: 116.1987 - val_loss: 0.9938 - val_k_smape: 114.5091\n",
      "Epoch 7/40\n",
      "13s - loss: 1.0096 - k_smape: 115.5930 - val_loss: 0.9959 - val_k_smape: 114.6559\n",
      "Epoch 8/40\n",
      "13s - loss: 1.0051 - k_smape: 115.1353 - val_loss: 0.9888 - val_k_smape: 113.7509\n",
      "Epoch 9/40\n",
      "13s - loss: 1.0003 - k_smape: 114.4419 - val_loss: 0.9864 - val_k_smape: 112.6896\n",
      "Epoch 10/40\n",
      "13s - loss: 0.9975 - k_smape: 114.2057 - val_loss: 0.9843 - val_k_smape: 112.5657\n",
      "Epoch 11/40\n",
      "13s - loss: 0.9920 - k_smape: 113.6268 - val_loss: 0.9835 - val_k_smape: 113.6621\n",
      "Epoch 12/40\n",
      "13s - loss: 0.9884 - k_smape: 113.2091 - val_loss: 0.9798 - val_k_smape: 113.3035\n",
      "Epoch 13/40\n",
      "13s - loss: 0.9843 - k_smape: 112.8840 - val_loss: 0.9845 - val_k_smape: 113.4310\n",
      "Epoch 14/40\n",
      "13s - loss: 0.9814 - k_smape: 112.6135 - val_loss: 0.9787 - val_k_smape: 113.8322\n",
      "Epoch 15/40\n",
      "13s - loss: 0.9783 - k_smape: 112.3220 - val_loss: 0.9760 - val_k_smape: 112.5396\n",
      "Epoch 16/40\n",
      "13s - loss: 0.9732 - k_smape: 111.8022 - val_loss: 0.9762 - val_k_smape: 112.7366\n",
      "Epoch 17/40\n",
      "13s - loss: 0.9714 - k_smape: 111.6732 - val_loss: 0.9734 - val_k_smape: 112.7201\n",
      "Epoch 18/40\n",
      "13s - loss: 0.9683 - k_smape: 111.4769 - val_loss: 0.9743 - val_k_smape: 111.4470\n",
      "Epoch 19/40\n",
      "13s - loss: 0.9659 - k_smape: 111.1620 - val_loss: 0.9713 - val_k_smape: 111.9673\n",
      "Epoch 20/40\n",
      "13s - loss: 0.9621 - k_smape: 110.7967 - val_loss: 0.9762 - val_k_smape: 112.2712\n",
      "Epoch 21/40\n",
      "13s - loss: 0.9604 - k_smape: 110.7499 - val_loss: 0.9735 - val_k_smape: 114.1998\n",
      "Epoch 22/40\n",
      "13s - loss: 0.9589 - k_smape: 110.7215 - val_loss: 0.9711 - val_k_smape: 111.4708\n",
      "Epoch 23/40\n",
      "13s - loss: 0.9548 - k_smape: 110.4092 - val_loss: 0.9683 - val_k_smape: 112.2105\n",
      "Epoch 24/40\n",
      "13s - loss: 0.9529 - k_smape: 110.1828 - val_loss: 0.9680 - val_k_smape: 111.4079\n",
      "Epoch 25/40\n",
      "13s - loss: 0.9516 - k_smape: 110.0842 - val_loss: 0.9652 - val_k_smape: 111.2529\n",
      "Epoch 26/40\n",
      "13s - loss: 0.9492 - k_smape: 109.9754 - val_loss: 0.9723 - val_k_smape: 112.9554\n",
      "Epoch 27/40\n",
      "13s - loss: 0.9452 - k_smape: 109.7069 - val_loss: 0.9691 - val_k_smape: 111.5601\n",
      "Epoch 28/40\n",
      "13s - loss: 0.9464 - k_smape: 109.7535 - val_loss: 0.9648 - val_k_smape: 111.6629\n",
      "Epoch 29/40\n",
      "13s - loss: 0.9434 - k_smape: 109.4754 - val_loss: 0.9630 - val_k_smape: 110.4871\n",
      "Epoch 30/40\n",
      "13s - loss: 0.9405 - k_smape: 109.2485 - val_loss: 0.9639 - val_k_smape: 111.0655\n",
      "Epoch 31/40\n",
      "14s - loss: 0.9396 - k_smape: 109.3110 - val_loss: 0.9656 - val_k_smape: 111.4603\n",
      "Epoch 32/40\n",
      "14s - loss: 0.9365 - k_smape: 109.0014 - val_loss: 0.9628 - val_k_smape: 111.2493\n",
      "Epoch 33/40\n",
      "14s - loss: 0.9358 - k_smape: 108.9089 - val_loss: 0.9641 - val_k_smape: 112.3134\n",
      "Epoch 34/40\n",
      "14s - loss: 0.9337 - k_smape: 108.7294 - val_loss: 0.9612 - val_k_smape: 111.1994\n",
      "Epoch 35/40\n",
      "13s - loss: 0.9321 - k_smape: 108.6964 - val_loss: 0.9595 - val_k_smape: 110.8728\n",
      "Epoch 36/40\n",
      "13s - loss: 0.9301 - k_smape: 108.5046 - val_loss: 0.9639 - val_k_smape: 111.1358\n",
      "Epoch 37/40\n",
      "13s - loss: 0.9285 - k_smape: 108.2477 - val_loss: 0.9648 - val_k_smape: 112.5834\n",
      "Epoch 38/40\n",
      "14s - loss: 0.9272 - k_smape: 108.2389 - val_loss: 0.9629 - val_k_smape: 112.1924\n",
      "Epoch 39/40\n",
      "14s - loss: 0.9256 - k_smape: 108.2564 - val_loss: 0.9671 - val_k_smape: 112.5264\n",
      "Epoch 40/40\n",
      "14s - loss: 0.9233 - k_smape: 108.0549 - val_loss: 0.9599 - val_k_smape: 110.4852\n",
      "--- Group: 4 / 4 ---\n",
      "Train on 635 samples, validate on 159 samples\n",
      "Epoch 1/60\n",
      "0s - loss: 1.2887 - k_smape: 172.6049 - val_loss: 1.1440 - val_k_smape: 171.4775\n",
      "Epoch 2/60\n",
      "0s - loss: 1.2711 - k_smape: 163.6087 - val_loss: 1.1283 - val_k_smape: 165.1094\n",
      "Epoch 3/60\n",
      "0s - loss: 1.2548 - k_smape: 157.2940 - val_loss: 1.1125 - val_k_smape: 158.2491\n",
      "Epoch 4/60\n",
      "0s - loss: 1.2398 - k_smape: 151.5987 - val_loss: 1.0950 - val_k_smape: 153.1187\n",
      "Epoch 5/60\n",
      "0s - loss: 1.2158 - k_smape: 145.8071 - val_loss: 1.0736 - val_k_smape: 147.4628\n",
      "Epoch 6/60\n",
      "0s - loss: 1.1990 - k_smape: 141.3225 - val_loss: 1.0549 - val_k_smape: 142.5997\n",
      "Epoch 7/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s - loss: 1.1763 - k_smape: 137.5010 - val_loss: 1.0347 - val_k_smape: 136.8749\n",
      "Epoch 8/60\n",
      "0s - loss: 1.1550 - k_smape: 132.7076 - val_loss: 1.0180 - val_k_smape: 132.9849\n",
      "Epoch 9/60\n",
      "0s - loss: 1.1356 - k_smape: 129.0349 - val_loss: 1.0049 - val_k_smape: 131.1554\n",
      "Epoch 10/60\n",
      "0s - loss: 1.1192 - k_smape: 128.1690 - val_loss: 0.9977 - val_k_smape: 129.7371\n",
      "Epoch 11/60\n",
      "0s - loss: 1.1069 - k_smape: 124.9970 - val_loss: 0.9875 - val_k_smape: 128.2727\n",
      "Epoch 12/60\n",
      "0s - loss: 1.0940 - k_smape: 121.6842 - val_loss: 0.9806 - val_k_smape: 126.5921\n",
      "Epoch 13/60\n",
      "0s - loss: 1.0764 - k_smape: 121.5013 - val_loss: 0.9681 - val_k_smape: 126.6786\n",
      "Epoch 14/60\n",
      "0s - loss: 1.0623 - k_smape: 119.1700 - val_loss: 0.9560 - val_k_smape: 123.8053\n",
      "Epoch 15/60\n",
      "0s - loss: 1.0414 - k_smape: 116.4087 - val_loss: 0.9583 - val_k_smape: 122.3508\n",
      "Epoch 16/60\n",
      "0s - loss: 1.0366 - k_smape: 114.5312 - val_loss: 0.9523 - val_k_smape: 122.1795\n",
      "Epoch 17/60\n",
      "0s - loss: 1.0221 - k_smape: 113.8121 - val_loss: 0.9483 - val_k_smape: 121.5421\n",
      "Epoch 18/60\n",
      "0s - loss: 1.0043 - k_smape: 110.4418 - val_loss: 0.9364 - val_k_smape: 120.5254\n",
      "Epoch 19/60\n",
      "0s - loss: 1.0055 - k_smape: 112.8486 - val_loss: 0.9394 - val_k_smape: 118.2360\n",
      "Epoch 20/60\n",
      "0s - loss: 0.9958 - k_smape: 108.0780 - val_loss: 0.9368 - val_k_smape: 119.5955\n",
      "Epoch 21/60\n",
      "0s - loss: 0.9809 - k_smape: 109.1022 - val_loss: 0.9368 - val_k_smape: 118.5594\n",
      "Epoch 22/60\n",
      "0s - loss: 0.9666 - k_smape: 106.8549 - val_loss: 0.9183 - val_k_smape: 116.9163\n",
      "Epoch 23/60\n",
      "0s - loss: 0.9586 - k_smape: 105.8246 - val_loss: 0.9126 - val_k_smape: 116.0980\n",
      "Epoch 24/60\n",
      "0s - loss: 0.9477 - k_smape: 105.3531 - val_loss: 0.9001 - val_k_smape: 115.1472\n",
      "Epoch 25/60\n",
      "0s - loss: 0.9480 - k_smape: 106.6947 - val_loss: 0.8961 - val_k_smape: 114.5044\n",
      "Epoch 26/60\n",
      "0s - loss: 0.9310 - k_smape: 102.9152 - val_loss: 0.9022 - val_k_smape: 115.3555\n",
      "Epoch 27/60\n",
      "0s - loss: 0.9273 - k_smape: 105.2732 - val_loss: 0.8970 - val_k_smape: 114.9457\n",
      "Epoch 28/60\n",
      "0s - loss: 0.9107 - k_smape: 100.8957 - val_loss: 0.9194 - val_k_smape: 116.4784\n",
      "Epoch 29/60\n",
      "0s - loss: 0.9194 - k_smape: 101.9712 - val_loss: 0.8946 - val_k_smape: 113.6409\n",
      "Epoch 30/60\n",
      "0s - loss: 0.8993 - k_smape: 102.1082 - val_loss: 0.9042 - val_k_smape: 117.6968\n",
      "Epoch 31/60\n",
      "0s - loss: 0.8904 - k_smape: 101.5856 - val_loss: 0.8856 - val_k_smape: 112.4776\n",
      "Epoch 32/60\n",
      "0s - loss: 0.8942 - k_smape: 100.2096 - val_loss: 0.8967 - val_k_smape: 113.7194\n",
      "Epoch 33/60\n",
      "0s - loss: 0.8858 - k_smape: 99.7946 - val_loss: 0.8882 - val_k_smape: 111.9546\n",
      "Epoch 34/60\n",
      "0s - loss: 0.8740 - k_smape: 98.1739 - val_loss: 0.8838 - val_k_smape: 111.9819\n",
      "Epoch 35/60\n",
      "0s - loss: 0.8685 - k_smape: 98.5918 - val_loss: 0.8866 - val_k_smape: 111.3463\n",
      "Epoch 36/60\n",
      "0s - loss: 0.8766 - k_smape: 98.1790 - val_loss: 0.8817 - val_k_smape: 111.2201\n",
      "Epoch 37/60\n",
      "0s - loss: 0.8701 - k_smape: 98.9880 - val_loss: 0.8785 - val_k_smape: 111.8808\n",
      "Epoch 38/60\n",
      "0s - loss: 0.8737 - k_smape: 98.6993 - val_loss: 0.8801 - val_k_smape: 111.0717\n",
      "Epoch 39/60\n",
      "0s - loss: 0.8577 - k_smape: 96.4118 - val_loss: 0.8788 - val_k_smape: 110.3218\n",
      "Epoch 40/60\n",
      "0s - loss: 0.8549 - k_smape: 97.1572 - val_loss: 0.8772 - val_k_smape: 112.9415\n",
      "Epoch 41/60\n",
      "0s - loss: 0.8403 - k_smape: 97.3962 - val_loss: 0.8675 - val_k_smape: 109.2610\n",
      "Epoch 42/60\n",
      "0s - loss: 0.8539 - k_smape: 97.0819 - val_loss: 0.8861 - val_k_smape: 110.8761\n",
      "Epoch 43/60\n",
      "0s - loss: 0.8438 - k_smape: 95.8088 - val_loss: 0.8759 - val_k_smape: 111.2196\n",
      "Epoch 44/60\n",
      "0s - loss: 0.8412 - k_smape: 95.9401 - val_loss: 0.8782 - val_k_smape: 111.7614\n",
      "Epoch 45/60\n",
      "0s - loss: 0.8423 - k_smape: 95.6873 - val_loss: 0.8793 - val_k_smape: 111.5013\n",
      "Epoch 46/60\n",
      "0s - loss: 0.8358 - k_smape: 95.2034 - val_loss: 0.8700 - val_k_smape: 110.3530\n",
      "Epoch 47/60\n",
      "0s - loss: 0.8331 - k_smape: 96.5346 - val_loss: 0.8793 - val_k_smape: 110.4945\n",
      "Epoch 48/60\n",
      "0s - loss: 0.8220 - k_smape: 94.5462 - val_loss: 0.8642 - val_k_smape: 108.4891\n",
      "Epoch 49/60\n",
      "0s - loss: 0.8140 - k_smape: 92.2911 - val_loss: 0.8748 - val_k_smape: 109.5956\n",
      "Epoch 50/60\n",
      "0s - loss: 0.8175 - k_smape: 93.6078 - val_loss: 0.8691 - val_k_smape: 109.2722\n",
      "Epoch 51/60\n",
      "0s - loss: 0.8061 - k_smape: 92.4223 - val_loss: 0.8754 - val_k_smape: 109.2952\n",
      "Epoch 52/60\n",
      "0s - loss: 0.8075 - k_smape: 93.8275 - val_loss: 0.8766 - val_k_smape: 109.6243\n",
      "Epoch 53/60\n",
      "0s - loss: 0.7910 - k_smape: 92.7536 - val_loss: 0.8750 - val_k_smape: 109.2338\n",
      "Epoch 54/60\n",
      "0s - loss: 0.8117 - k_smape: 93.3548 - val_loss: 0.8718 - val_k_smape: 109.9335\n",
      "Epoch 55/60\n",
      "0s - loss: 0.7977 - k_smape: 92.1039 - val_loss: 0.8599 - val_k_smape: 107.2482\n",
      "Epoch 56/60\n",
      "0s - loss: 0.7998 - k_smape: 92.4162 - val_loss: 0.8719 - val_k_smape: 109.0188\n",
      "Epoch 57/60\n",
      "0s - loss: 0.8012 - k_smape: 93.1415 - val_loss: 0.8586 - val_k_smape: 107.3108\n",
      "Epoch 58/60\n",
      "0s - loss: 0.7911 - k_smape: 91.3988 - val_loss: 0.8578 - val_k_smape: 107.5180\n",
      "Epoch 59/60\n",
      "0s - loss: 0.7826 - k_smape: 90.8061 - val_loss: 0.8653 - val_k_smape: 107.8318\n",
      "Epoch 60/60\n",
      "0s - loss: 0.7740 - k_smape: 89.9827 - val_loss: 0.8608 - val_k_smape: 107.2265\n",
      "=== Run: 2 / 5 ===\n",
      "--- Group: 1 / 4 ---\n",
      "Train on 21930 samples, validate on 5483 samples\n",
      "--- Group: 2 / 4 ---\n",
      "Train on 32032 samples, validate on 8009 samples\n",
      "Epoch 1/40\n",
      "7s - loss: 1.0633 - k_smape: 156.8959 - val_loss: 1.0372 - val_k_smape: 151.5853\n",
      "Epoch 2/40\n",
      "7s - loss: 1.0418 - k_smape: 148.7636 - val_loss: 1.0285 - val_k_smape: 147.8378\n",
      "Epoch 3/40\n",
      "7s - loss: 1.0342 - k_smape: 146.4737 - val_loss: 1.0237 - val_k_smape: 145.8790\n",
      "Epoch 4/40\n",
      "7s - loss: 1.0299 - k_smape: 145.4310 - val_loss: 1.0221 - val_k_smape: 142.9412\n",
      "Epoch 5/40\n",
      "7s - loss: 1.0251 - k_smape: 144.4337 - val_loss: 1.0166 - val_k_smape: 143.8508\n",
      "Epoch 6/40\n",
      "7s - loss: 1.0210 - k_smape: 143.2306 - val_loss: 1.0145 - val_k_smape: 141.7464\n",
      "Epoch 7/40\n",
      "7s - loss: 1.0177 - k_smape: 142.6403 - val_loss: 1.0155 - val_k_smape: 144.7614\n",
      "Epoch 8/40\n",
      "7s - loss: 1.0158 - k_smape: 142.2165 - val_loss: 1.0119 - val_k_smape: 142.9212\n",
      "Epoch 9/40\n",
      "7s - loss: 1.0121 - k_smape: 141.5074 - val_loss: 1.0145 - val_k_smape: 140.1774\n",
      "Epoch 10/40\n",
      "7s - loss: 1.0092 - k_smape: 141.0969 - val_loss: 1.0120 - val_k_smape: 143.0316\n",
      "Epoch 11/40\n",
      "7s - loss: 1.0054 - k_smape: 140.4831 - val_loss: 1.0116 - val_k_smape: 142.8558\n",
      "Epoch 12/40\n",
      "7s - loss: 1.0039 - k_smape: 140.1637 - val_loss: 1.0097 - val_k_smape: 141.3255\n",
      "Epoch 13/40\n",
      "7s - loss: 1.0008 - k_smape: 139.7120 - val_loss: 1.0136 - val_k_smape: 144.1533\n",
      "Epoch 14/40\n",
      "7s - loss: 0.9994 - k_smape: 139.7351 - val_loss: 1.0112 - val_k_smape: 141.1655\n",
      "Epoch 15/40\n",
      "7s - loss: 0.9959 - k_smape: 139.2135 - val_loss: 1.0113 - val_k_smape: 143.5897\n",
      "Epoch 16/40\n",
      "7s - loss: 0.9928 - k_smape: 138.8424 - val_loss: 1.0102 - val_k_smape: 142.5675\n",
      "Epoch 17/40\n",
      "7s - loss: 0.9891 - k_smape: 138.3529 - val_loss: 1.0099 - val_k_smape: 142.3773\n",
      "Epoch 18/40\n",
      "7s - loss: 0.9889 - k_smape: 138.3179 - val_loss: 1.0132 - val_k_smape: 142.4412\n",
      "Epoch 19/40\n",
      "7s - loss: 0.9858 - k_smape: 138.1136 - val_loss: 1.0090 - val_k_smape: 142.8381\n",
      "Epoch 20/40\n",
      "7s - loss: 0.9836 - k_smape: 137.8401 - val_loss: 1.0109 - val_k_smape: 141.9130\n",
      "Epoch 21/40\n",
      "7s - loss: 0.9818 - k_smape: 137.6337 - val_loss: 1.0115 - val_k_smape: 140.6252\n",
      "Epoch 22/40\n",
      "7s - loss: 0.9777 - k_smape: 137.0648 - val_loss: 1.0090 - val_k_smape: 140.9331\n",
      "Epoch 23/40\n",
      "7s - loss: 0.9755 - k_smape: 137.1284 - val_loss: 1.0112 - val_k_smape: 141.7799\n",
      "Epoch 24/40\n",
      "7s - loss: 0.9737 - k_smape: 136.8374 - val_loss: 1.0117 - val_k_smape: 140.1679\n",
      "Epoch 25/40\n",
      "7s - loss: 0.9719 - k_smape: 136.7580 - val_loss: 1.0101 - val_k_smape: 139.8580\n",
      "Epoch 26/40\n",
      "7s - loss: 0.9698 - k_smape: 136.5091 - val_loss: 1.0125 - val_k_smape: 140.5516\n",
      "Epoch 27/40\n",
      "7s - loss: 0.9669 - k_smape: 136.4783 - val_loss: 1.0120 - val_k_smape: 140.0328\n",
      "Epoch 28/40\n",
      "7s - loss: 0.9662 - k_smape: 136.3654 - val_loss: 1.0109 - val_k_smape: 139.1177\n",
      "Epoch 29/40\n",
      "7s - loss: 0.9633 - k_smape: 135.9479 - val_loss: 1.0108 - val_k_smape: 141.4218\n",
      "Epoch 30/40\n",
      "7s - loss: 0.9633 - k_smape: 136.0801 - val_loss: 1.0088 - val_k_smape: 140.2959\n",
      "Epoch 31/40\n",
      "7s - loss: 0.9612 - k_smape: 135.7922 - val_loss: 1.0111 - val_k_smape: 141.7254\n",
      "Epoch 32/40\n",
      "7s - loss: 0.9599 - k_smape: 135.8666 - val_loss: 1.0140 - val_k_smape: 141.1503\n",
      "Epoch 33/40\n",
      "7s - loss: 0.9572 - k_smape: 135.5071 - val_loss: 1.0128 - val_k_smape: 140.6073\n",
      "Epoch 34/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7s - loss: 0.9561 - k_smape: 135.4522 - val_loss: 1.0127 - val_k_smape: 139.5956\n",
      "Epoch 35/40\n",
      "7s - loss: 0.9541 - k_smape: 135.3089 - val_loss: 1.0101 - val_k_smape: 140.7823\n",
      "Epoch 36/40\n",
      "7s - loss: 0.9528 - k_smape: 135.3722 - val_loss: 1.0099 - val_k_smape: 139.8062\n",
      "Epoch 37/40\n",
      "7s - loss: 0.9507 - k_smape: 135.1830 - val_loss: 1.0129 - val_k_smape: 140.6150\n",
      "Epoch 38/40\n",
      "7s - loss: 0.9492 - k_smape: 134.7009 - val_loss: 1.0110 - val_k_smape: 140.3058\n",
      "Epoch 39/40\n",
      "7s - loss: 0.9499 - k_smape: 134.6235 - val_loss: 1.0094 - val_k_smape: 140.8899\n",
      "Epoch 40/40\n",
      "7s - loss: 0.9482 - k_smape: 134.6945 - val_loss: 1.0153 - val_k_smape: 141.3186\n",
      "--- Group: 3 / 4 ---\n",
      "Train on 61452 samples, validate on 15363 samples\n",
      "Epoch 1/40\n",
      "14s - loss: 1.0899 - k_smape: 128.7043 - val_loss: 1.0432 - val_k_smape: 121.9628\n",
      "Epoch 2/40\n",
      "13s - loss: 1.0513 - k_smape: 121.5327 - val_loss: 1.0317 - val_k_smape: 119.5025\n",
      "Epoch 3/40\n",
      "13s - loss: 1.0395 - k_smape: 119.6493 - val_loss: 1.0186 - val_k_smape: 115.3660\n",
      "Epoch 4/40\n",
      "13s - loss: 1.0316 - k_smape: 118.6315 - val_loss: 1.0129 - val_k_smape: 116.9404\n",
      "Epoch 5/40\n",
      "13s - loss: 1.0249 - k_smape: 117.8459 - val_loss: 1.0094 - val_k_smape: 114.4029\n",
      "Epoch 6/40\n",
      "13s - loss: 1.0193 - k_smape: 116.9368 - val_loss: 1.0042 - val_k_smape: 115.2195\n",
      "Epoch 7/40\n",
      "13s - loss: 1.0148 - k_smape: 116.4583 - val_loss: 0.9994 - val_k_smape: 112.9815\n",
      "Epoch 8/40\n",
      "13s - loss: 1.0107 - k_smape: 116.0192 - val_loss: 0.9984 - val_k_smape: 114.7840\n",
      "Epoch 9/40\n",
      "13s - loss: 1.0064 - k_smape: 115.4687 - val_loss: 0.9967 - val_k_smape: 114.2567\n",
      "Epoch 10/40\n",
      "13s - loss: 1.0039 - k_smape: 115.2501 - val_loss: 0.9922 - val_k_smape: 112.9479\n",
      "Epoch 11/40\n",
      "13s - loss: 1.0015 - k_smape: 114.8598 - val_loss: 0.9979 - val_k_smape: 114.8001\n",
      "Epoch 12/40\n",
      "13s - loss: 0.9979 - k_smape: 114.4059 - val_loss: 0.9913 - val_k_smape: 113.5972\n",
      "Epoch 13/40\n",
      "13s - loss: 0.9943 - k_smape: 114.1097 - val_loss: 0.9893 - val_k_smape: 113.0641\n",
      "Epoch 14/40\n",
      "13s - loss: 0.9919 - k_smape: 113.9051 - val_loss: 0.9876 - val_k_smape: 113.8932\n",
      "Epoch 15/40\n",
      "13s - loss: 0.9886 - k_smape: 113.5751 - val_loss: 0.9866 - val_k_smape: 113.5547\n",
      "Epoch 16/40\n",
      "13s - loss: 0.9850 - k_smape: 113.1243 - val_loss: 0.9926 - val_k_smape: 116.1505\n",
      "Epoch 17/40\n",
      "13s - loss: 0.9828 - k_smape: 112.9868 - val_loss: 0.9841 - val_k_smape: 113.9323\n",
      "Epoch 18/40\n",
      "13s - loss: 0.9815 - k_smape: 112.9692 - val_loss: 0.9834 - val_k_smape: 111.6488\n",
      "Epoch 19/40\n",
      "13s - loss: 0.9777 - k_smape: 112.5762 - val_loss: 0.9828 - val_k_smape: 112.0472\n",
      "Epoch 20/40\n",
      "13s - loss: 0.9752 - k_smape: 112.2732 - val_loss: 0.9837 - val_k_smape: 112.8277\n",
      "Epoch 21/40\n",
      "13s - loss: 0.9731 - k_smape: 112.1039 - val_loss: 0.9822 - val_k_smape: 111.7693\n",
      "Epoch 22/40\n",
      "13s - loss: 0.9712 - k_smape: 112.0330 - val_loss: 0.9817 - val_k_smape: 112.4900\n",
      "Epoch 23/40\n",
      "13s - loss: 0.9689 - k_smape: 111.8562 - val_loss: 0.9776 - val_k_smape: 110.2083\n",
      "Epoch 24/40\n",
      "13s - loss: 0.9666 - k_smape: 111.6139 - val_loss: 0.9822 - val_k_smape: 112.9495\n",
      "Epoch 25/40\n",
      "13s - loss: 0.9643 - k_smape: 111.4872 - val_loss: 0.9817 - val_k_smape: 114.6449\n",
      "Epoch 26/40\n",
      "13s - loss: 0.9629 - k_smape: 111.2389 - val_loss: 0.9817 - val_k_smape: 113.6021\n",
      "Epoch 27/40\n",
      "13s - loss: 0.9609 - k_smape: 111.1197 - val_loss: 0.9757 - val_k_smape: 111.4572\n",
      "Epoch 28/40\n",
      "13s - loss: 0.9579 - k_smape: 110.8186 - val_loss: 0.9809 - val_k_smape: 113.1158\n",
      "Epoch 29/40\n",
      "13s - loss: 0.9571 - k_smape: 110.7714 - val_loss: 0.9761 - val_k_smape: 111.6908\n",
      "Epoch 30/40\n",
      "13s - loss: 0.9556 - k_smape: 110.6669 - val_loss: 0.9759 - val_k_smape: 112.5402\n",
      "Epoch 31/40\n",
      "13s - loss: 0.9525 - k_smape: 110.4471 - val_loss: 0.9778 - val_k_smape: 112.1106\n",
      "Epoch 32/40\n",
      "13s - loss: 0.9520 - k_smape: 110.3583 - val_loss: 0.9730 - val_k_smape: 111.2739\n",
      "Epoch 33/40\n",
      "13s - loss: 0.9509 - k_smape: 110.1706 - val_loss: 0.9754 - val_k_smape: 110.9214\n",
      "Epoch 34/40\n",
      "13s - loss: 0.9484 - k_smape: 109.9088 - val_loss: 0.9745 - val_k_smape: 112.6161\n",
      "Epoch 35/40\n",
      "13s - loss: 0.9466 - k_smape: 109.9670 - val_loss: 0.9725 - val_k_smape: 111.2158\n",
      "Epoch 36/40\n",
      "13s - loss: 0.9443 - k_smape: 109.8655 - val_loss: 0.9740 - val_k_smape: 111.7130\n",
      "Epoch 37/40\n",
      "13s - loss: 0.9433 - k_smape: 109.7972 - val_loss: 0.9784 - val_k_smape: 112.2457\n",
      "Epoch 38/40\n",
      "13s - loss: 0.9422 - k_smape: 109.7557 - val_loss: 0.9742 - val_k_smape: 111.2294\n",
      "Epoch 39/40\n",
      "13s - loss: 0.9409 - k_smape: 109.5764 - val_loss: 0.9725 - val_k_smape: 111.2597\n",
      "Epoch 40/40\n",
      "13s - loss: 0.9389 - k_smape: 109.4209 - val_loss: 0.9719 - val_k_smape: 112.2007\n",
      "--- Group: 4 / 4 ---\n",
      "Train on 635 samples, validate on 159 samples\n",
      "Epoch 1/60\n",
      "0s - loss: 1.2507 - k_smape: 174.8593 - val_loss: 1.3156 - val_k_smape: 178.8342\n",
      "Epoch 2/60\n",
      "0s - loss: 1.2380 - k_smape: 168.4766 - val_loss: 1.3022 - val_k_smape: 170.9905\n",
      "Epoch 3/60\n",
      "0s - loss: 1.2243 - k_smape: 159.5484 - val_loss: 1.2881 - val_k_smape: 165.3724\n",
      "Epoch 4/60\n",
      "0s - loss: 1.2098 - k_smape: 155.7985 - val_loss: 1.2742 - val_k_smape: 160.9801\n",
      "Epoch 5/60\n",
      "0s - loss: 1.1900 - k_smape: 149.4925 - val_loss: 1.2508 - val_k_smape: 152.3103\n",
      "Epoch 6/60\n",
      "0s - loss: 1.1724 - k_smape: 142.8311 - val_loss: 1.2327 - val_k_smape: 147.5432\n",
      "Epoch 7/60\n",
      "0s - loss: 1.1543 - k_smape: 139.9323 - val_loss: 1.2200 - val_k_smape: 145.4142\n",
      "Epoch 8/60\n",
      "0s - loss: 1.1315 - k_smape: 135.7395 - val_loss: 1.1987 - val_k_smape: 139.4527\n",
      "Epoch 9/60\n",
      "0s - loss: 1.1102 - k_smape: 132.5041 - val_loss: 1.1857 - val_k_smape: 136.7296\n",
      "Epoch 10/60\n",
      "0s - loss: 1.0964 - k_smape: 128.1274 - val_loss: 1.1799 - val_k_smape: 135.7378\n",
      "Epoch 11/60\n",
      "0s - loss: 1.0848 - k_smape: 129.1732 - val_loss: 1.1716 - val_k_smape: 133.2520\n",
      "Epoch 12/60\n",
      "0s - loss: 1.0608 - k_smape: 122.0781 - val_loss: 1.1565 - val_k_smape: 129.4219\n",
      "Epoch 13/60\n",
      "0s - loss: 1.0391 - k_smape: 121.1830 - val_loss: 1.1458 - val_k_smape: 128.0816\n",
      "Epoch 14/60\n",
      "0s - loss: 1.0219 - k_smape: 117.3801 - val_loss: 1.1262 - val_k_smape: 124.2475\n",
      "Epoch 15/60\n",
      "0s - loss: 1.0158 - k_smape: 116.9563 - val_loss: 1.1384 - val_k_smape: 127.4443\n",
      "Epoch 16/60\n",
      "0s - loss: 0.9957 - k_smape: 113.6575 - val_loss: 1.1120 - val_k_smape: 122.6023\n",
      "Epoch 17/60\n",
      "0s - loss: 0.9838 - k_smape: 113.1790 - val_loss: 1.1134 - val_k_smape: 122.9669\n",
      "Epoch 18/60\n",
      "0s - loss: 0.9745 - k_smape: 110.6152 - val_loss: 1.1002 - val_k_smape: 121.4044\n",
      "Epoch 19/60\n",
      "0s - loss: 0.9593 - k_smape: 111.1418 - val_loss: 1.0838 - val_k_smape: 119.1581\n",
      "Epoch 20/60\n",
      "0s - loss: 0.9477 - k_smape: 108.7206 - val_loss: 1.0862 - val_k_smape: 119.0481\n",
      "Epoch 21/60\n",
      "0s - loss: 0.9448 - k_smape: 109.0767 - val_loss: 1.0834 - val_k_smape: 117.3049\n",
      "Epoch 22/60\n",
      "0s - loss: 0.9320 - k_smape: 106.6851 - val_loss: 1.0942 - val_k_smape: 120.4021\n",
      "Epoch 23/60\n",
      "0s - loss: 0.9367 - k_smape: 107.7375 - val_loss: 1.0744 - val_k_smape: 115.5914\n",
      "Epoch 24/60\n",
      "0s - loss: 0.9260 - k_smape: 104.4053 - val_loss: 1.0725 - val_k_smape: 116.0230\n",
      "Epoch 25/60\n",
      "0s - loss: 0.9119 - k_smape: 105.7866 - val_loss: 1.0794 - val_k_smape: 118.6017\n",
      "Epoch 26/60\n",
      "0s - loss: 0.8966 - k_smape: 102.3994 - val_loss: 1.0509 - val_k_smape: 112.7200\n",
      "Epoch 27/60\n",
      "0s - loss: 0.8904 - k_smape: 102.8631 - val_loss: 1.0593 - val_k_smape: 113.2598\n",
      "Epoch 28/60\n",
      "0s - loss: 0.8946 - k_smape: 102.4790 - val_loss: 1.0759 - val_k_smape: 116.6228\n",
      "Epoch 29/60\n",
      "0s - loss: 0.8870 - k_smape: 102.4715 - val_loss: 1.0653 - val_k_smape: 115.1705\n",
      "Epoch 30/60\n",
      "0s - loss: 0.8861 - k_smape: 102.1996 - val_loss: 1.0623 - val_k_smape: 114.1592\n",
      "Epoch 31/60\n",
      "0s - loss: 0.8632 - k_smape: 99.5138 - val_loss: 1.0431 - val_k_smape: 111.0520\n",
      "Epoch 32/60\n",
      "0s - loss: 0.8662 - k_smape: 99.5918 - val_loss: 1.0565 - val_k_smape: 114.5314\n",
      "Epoch 33/60\n",
      "0s - loss: 0.8685 - k_smape: 100.5757 - val_loss: 1.0504 - val_k_smape: 111.9245\n",
      "Epoch 34/60\n",
      "0s - loss: 0.8631 - k_smape: 100.2019 - val_loss: 1.0618 - val_k_smape: 114.7622\n",
      "Epoch 35/60\n",
      "0s - loss: 0.8517 - k_smape: 99.3983 - val_loss: 1.0427 - val_k_smape: 111.8811\n",
      "Epoch 36/60\n",
      "0s - loss: 0.8548 - k_smape: 98.6938 - val_loss: 1.0457 - val_k_smape: 111.6586\n",
      "Epoch 37/60\n",
      "0s - loss: 0.8458 - k_smape: 99.1637 - val_loss: 1.0520 - val_k_smape: 113.3271\n",
      "Epoch 38/60\n",
      "0s - loss: 0.8481 - k_smape: 97.9595 - val_loss: 1.0365 - val_k_smape: 110.9586\n",
      "Epoch 39/60\n",
      "0s - loss: 0.8378 - k_smape: 98.2924 - val_loss: 1.0439 - val_k_smape: 111.9068\n",
      "Epoch 40/60\n",
      "0s - loss: 0.8385 - k_smape: 96.8073 - val_loss: 1.0511 - val_k_smape: 112.2666\n",
      "Epoch 41/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s - loss: 0.8187 - k_smape: 95.3754 - val_loss: 1.0340 - val_k_smape: 111.0162\n",
      "Epoch 42/60\n",
      "0s - loss: 0.8322 - k_smape: 97.6277 - val_loss: 1.0355 - val_k_smape: 110.8952\n",
      "Epoch 43/60\n",
      "0s - loss: 0.8239 - k_smape: 96.7134 - val_loss: 1.0398 - val_k_smape: 110.8153\n",
      "Epoch 44/60\n",
      "0s - loss: 0.8147 - k_smape: 95.2384 - val_loss: 1.0409 - val_k_smape: 111.1483\n",
      "Epoch 45/60\n",
      "0s - loss: 0.8182 - k_smape: 96.6090 - val_loss: 1.0393 - val_k_smape: 111.5105\n",
      "Epoch 46/60\n",
      "0s - loss: 0.8075 - k_smape: 94.1120 - val_loss: 1.0345 - val_k_smape: 110.7909\n",
      "Epoch 47/60\n",
      "0s - loss: 0.8114 - k_smape: 95.4174 - val_loss: 1.0372 - val_k_smape: 111.0750\n",
      "Epoch 48/60\n",
      "0s - loss: 0.8023 - k_smape: 93.8509 - val_loss: 1.0245 - val_k_smape: 109.2201\n",
      "Epoch 49/60\n",
      "0s - loss: 0.8056 - k_smape: 94.8539 - val_loss: 1.0428 - val_k_smape: 112.3302\n",
      "Epoch 50/60\n",
      "0s - loss: 0.7996 - k_smape: 94.9753 - val_loss: 1.0285 - val_k_smape: 110.4772\n",
      "Epoch 51/60\n",
      "0s - loss: 0.7969 - k_smape: 94.5895 - val_loss: 1.0404 - val_k_smape: 110.7606\n",
      "Epoch 52/60\n",
      "0s - loss: 0.7962 - k_smape: 93.6641 - val_loss: 1.0283 - val_k_smape: 110.6987\n",
      "Epoch 53/60\n",
      "0s - loss: 0.7960 - k_smape: 93.7667 - val_loss: 1.0191 - val_k_smape: 109.5911\n",
      "Epoch 54/60\n",
      "0s - loss: 0.7856 - k_smape: 92.7003 - val_loss: 1.0214 - val_k_smape: 108.9386\n",
      "Epoch 55/60\n",
      "0s - loss: 0.7963 - k_smape: 92.9536 - val_loss: 1.0276 - val_k_smape: 108.7958\n",
      "Epoch 56/60\n",
      "0s - loss: 0.7801 - k_smape: 93.1388 - val_loss: 1.0174 - val_k_smape: 109.1278\n",
      "Epoch 57/60\n",
      "0s - loss: 0.7832 - k_smape: 92.7001 - val_loss: 1.0255 - val_k_smape: 108.9213\n",
      "Epoch 58/60\n",
      "0s - loss: 0.7743 - k_smape: 91.9689 - val_loss: 1.0267 - val_k_smape: 109.4836\n",
      "Epoch 59/60\n",
      "0s - loss: 0.7835 - k_smape: 93.0332 - val_loss: 1.0063 - val_k_smape: 106.0103\n",
      "Epoch 60/60\n",
      "0s - loss: 0.7790 - k_smape: 91.3951 - val_loss: 1.0321 - val_k_smape: 109.9643\n",
      "=== Run: 3 / 5 ===\n",
      "--- Group: 1 / 4 ---\n",
      "Train on 21930 samples, validate on 5483 samples\n",
      "--- Group: 2 / 4 ---\n",
      "Train on 32032 samples, validate on 8009 samples\n",
      "Epoch 1/40\n",
      "7s - loss: 1.0635 - k_smape: 156.1555 - val_loss: 1.0356 - val_k_smape: 153.5540\n",
      "Epoch 2/40\n",
      "7s - loss: 1.0421 - k_smape: 148.3783 - val_loss: 1.0199 - val_k_smape: 146.6167\n",
      "Epoch 3/40\n",
      "7s - loss: 1.0341 - k_smape: 146.4208 - val_loss: 1.0196 - val_k_smape: 146.3442\n",
      "Epoch 4/40\n",
      "7s - loss: 1.0289 - k_smape: 144.8739 - val_loss: 1.0191 - val_k_smape: 147.0656\n",
      "Epoch 5/40\n",
      "7s - loss: 1.0257 - k_smape: 144.2239 - val_loss: 1.0124 - val_k_smape: 144.3173\n",
      "Epoch 6/40\n",
      "7s - loss: 1.0204 - k_smape: 143.2474 - val_loss: 1.0133 - val_k_smape: 145.5477\n",
      "Epoch 7/40\n",
      "7s - loss: 1.0179 - k_smape: 142.6291 - val_loss: 1.0075 - val_k_smape: 141.5799\n",
      "Epoch 8/40\n",
      "7s - loss: 1.0148 - k_smape: 142.0720 - val_loss: 1.0077 - val_k_smape: 142.9821\n",
      "Epoch 9/40\n",
      "7s - loss: 1.0113 - k_smape: 141.4756 - val_loss: 1.0068 - val_k_smape: 140.4785\n",
      "Epoch 10/40\n",
      "7s - loss: 1.0087 - k_smape: 140.9689 - val_loss: 1.0101 - val_k_smape: 144.3670\n",
      "Epoch 11/40\n",
      "7s - loss: 1.0064 - k_smape: 140.6306 - val_loss: 1.0040 - val_k_smape: 140.2424\n",
      "Epoch 12/40\n",
      "7s - loss: 1.0024 - k_smape: 140.1802 - val_loss: 1.0056 - val_k_smape: 142.2167\n",
      "Epoch 13/40\n",
      "7s - loss: 0.9993 - k_smape: 139.6542 - val_loss: 1.0071 - val_k_smape: 142.6188\n",
      "Epoch 14/40\n",
      "7s - loss: 0.9970 - k_smape: 139.3392 - val_loss: 1.0070 - val_k_smape: 143.1149\n",
      "Epoch 15/40\n",
      "7s - loss: 0.9940 - k_smape: 139.0126 - val_loss: 1.0077 - val_k_smape: 141.9709\n",
      "Epoch 16/40\n",
      "7s - loss: 0.9927 - k_smape: 139.1236 - val_loss: 1.0092 - val_k_smape: 141.6437\n",
      "Epoch 17/40\n",
      "7s - loss: 0.9886 - k_smape: 138.5240 - val_loss: 1.0086 - val_k_smape: 142.7441\n",
      "Epoch 18/40\n",
      "7s - loss: 0.9865 - k_smape: 138.3585 - val_loss: 1.0052 - val_k_smape: 142.1350\n",
      "Epoch 19/40\n",
      "7s - loss: 0.9840 - k_smape: 137.9695 - val_loss: 1.0076 - val_k_smape: 144.2063\n",
      "Epoch 20/40\n",
      "7s - loss: 0.9809 - k_smape: 138.0197 - val_loss: 1.0073 - val_k_smape: 141.9454\n",
      "Epoch 21/40\n",
      "7s - loss: 0.9798 - k_smape: 137.8160 - val_loss: 1.0067 - val_k_smape: 142.1712\n",
      "Epoch 22/40\n",
      "7s - loss: 0.9773 - k_smape: 137.3738 - val_loss: 1.0092 - val_k_smape: 141.3022\n",
      "Epoch 23/40\n",
      "7s - loss: 0.9753 - k_smape: 137.2818 - val_loss: 1.0058 - val_k_smape: 141.2123\n",
      "Epoch 24/40\n",
      "7s - loss: 0.9731 - k_smape: 137.2180 - val_loss: 1.0098 - val_k_smape: 143.2813\n",
      "Epoch 25/40\n",
      "7s - loss: 0.9711 - k_smape: 136.9762 - val_loss: 1.0073 - val_k_smape: 143.2669\n",
      "Epoch 26/40\n",
      "7s - loss: 0.9684 - k_smape: 137.0595 - val_loss: 1.0110 - val_k_smape: 143.2255\n",
      "Epoch 27/40\n",
      "7s - loss: 0.9681 - k_smape: 136.8615 - val_loss: 1.0075 - val_k_smape: 142.5293\n",
      "Epoch 28/40\n",
      "7s - loss: 0.9655 - k_smape: 136.7117 - val_loss: 1.0094 - val_k_smape: 141.3178\n",
      "Epoch 29/40\n",
      "7s - loss: 0.9639 - k_smape: 136.6542 - val_loss: 1.0093 - val_k_smape: 140.8610\n",
      "Epoch 30/40\n",
      "7s - loss: 0.9620 - k_smape: 136.4343 - val_loss: 1.0083 - val_k_smape: 141.7989\n",
      "Epoch 31/40\n",
      "7s - loss: 0.9604 - k_smape: 136.3968 - val_loss: 1.0100 - val_k_smape: 143.7626\n",
      "Epoch 32/40\n",
      "7s - loss: 0.9597 - k_smape: 136.0675 - val_loss: 1.0086 - val_k_smape: 141.5646\n",
      "Epoch 33/40\n",
      "7s - loss: 0.9572 - k_smape: 135.5891 - val_loss: 1.0063 - val_k_smape: 141.5016\n",
      "Epoch 34/40\n",
      "7s - loss: 0.9555 - k_smape: 135.6432 - val_loss: 1.0068 - val_k_smape: 141.6356\n",
      "Epoch 35/40\n",
      "7s - loss: 0.9549 - k_smape: 135.6713 - val_loss: 1.0083 - val_k_smape: 140.7601\n",
      "Epoch 36/40\n",
      "7s - loss: 0.9546 - k_smape: 135.3575 - val_loss: 1.0085 - val_k_smape: 142.6125\n",
      "Epoch 37/40\n",
      "7s - loss: 0.9503 - k_smape: 135.2656 - val_loss: 1.0110 - val_k_smape: 141.2591\n",
      "Epoch 38/40\n",
      "7s - loss: 0.9508 - k_smape: 135.0527 - val_loss: 1.0096 - val_k_smape: 141.2481\n",
      "Epoch 39/40\n",
      "7s - loss: 0.9490 - k_smape: 134.9703 - val_loss: 1.0118 - val_k_smape: 141.4014\n",
      "Epoch 40/40\n",
      "7s - loss: 0.9462 - k_smape: 134.9583 - val_loss: 1.0076 - val_k_smape: 141.1628\n",
      "--- Group: 3 / 4 ---\n",
      "Train on 61452 samples, validate on 15363 samples\n",
      "Epoch 1/40\n",
      "14s - loss: 1.0896 - k_smape: 128.1436 - val_loss: 1.0302 - val_k_smape: 121.1361\n",
      "Epoch 2/40\n",
      "13s - loss: 1.0490 - k_smape: 120.7866 - val_loss: 1.0230 - val_k_smape: 120.8424\n",
      "Epoch 3/40\n",
      "13s - loss: 1.0356 - k_smape: 118.9038 - val_loss: 1.0061 - val_k_smape: 117.5768\n",
      "Epoch 4/40\n",
      "13s - loss: 1.0266 - k_smape: 117.6095 - val_loss: 0.9990 - val_k_smape: 114.2477\n",
      "Epoch 5/40\n",
      "13s - loss: 1.0194 - k_smape: 116.7521 - val_loss: 0.9926 - val_k_smape: 113.7756\n",
      "Epoch 6/40\n",
      "13s - loss: 1.0139 - k_smape: 116.0450 - val_loss: 0.9921 - val_k_smape: 114.7533\n",
      "Epoch 7/40\n",
      "13s - loss: 1.0086 - k_smape: 115.2812 - val_loss: 0.9877 - val_k_smape: 113.7828\n",
      "Epoch 8/40\n",
      "13s - loss: 1.0045 - k_smape: 114.9285 - val_loss: 0.9857 - val_k_smape: 113.4241\n",
      "Epoch 9/40\n",
      "13s - loss: 0.9994 - k_smape: 114.3102 - val_loss: 0.9820 - val_k_smape: 112.9828\n",
      "Epoch 10/40\n",
      "13s - loss: 0.9956 - k_smape: 113.9904 - val_loss: 0.9799 - val_k_smape: 112.5413\n",
      "Epoch 11/40\n",
      "13s - loss: 0.9919 - k_smape: 113.7084 - val_loss: 0.9840 - val_k_smape: 112.4948\n",
      "Epoch 12/40\n",
      "13s - loss: 0.9880 - k_smape: 113.2382 - val_loss: 0.9754 - val_k_smape: 112.2179\n",
      "Epoch 13/40\n",
      "13s - loss: 0.9837 - k_smape: 112.7861 - val_loss: 0.9733 - val_k_smape: 112.7418\n",
      "Epoch 14/40\n",
      "13s - loss: 0.9799 - k_smape: 112.4842 - val_loss: 0.9772 - val_k_smape: 112.0424\n",
      "Epoch 15/40\n",
      "13s - loss: 0.9792 - k_smape: 112.3264 - val_loss: 0.9761 - val_k_smape: 112.7951\n",
      "Epoch 16/40\n",
      "13s - loss: 0.9748 - k_smape: 111.9661 - val_loss: 0.9780 - val_k_smape: 112.5614\n",
      "Epoch 17/40\n",
      "13s - loss: 0.9706 - k_smape: 111.5334 - val_loss: 0.9811 - val_k_smape: 113.5639\n",
      "Epoch 18/40\n",
      "13s - loss: 0.9669 - k_smape: 111.1999 - val_loss: 0.9691 - val_k_smape: 111.9733\n",
      "Epoch 19/40\n",
      "13s - loss: 0.9655 - k_smape: 111.1403 - val_loss: 0.9676 - val_k_smape: 111.2020\n",
      "Epoch 20/40\n",
      "13s - loss: 0.9628 - k_smape: 110.9335 - val_loss: 0.9698 - val_k_smape: 112.9286\n",
      "Epoch 21/40\n",
      "13s - loss: 0.9610 - k_smape: 110.8169 - val_loss: 0.9701 - val_k_smape: 112.2422\n",
      "Epoch 22/40\n",
      "13s - loss: 0.9579 - k_smape: 110.4227 - val_loss: 0.9702 - val_k_smape: 111.7950\n",
      "Epoch 23/40\n",
      "13s - loss: 0.9551 - k_smape: 110.2489 - val_loss: 0.9667 - val_k_smape: 111.5907\n",
      "Epoch 24/40\n",
      "13s - loss: 0.9517 - k_smape: 110.0675 - val_loss: 0.9674 - val_k_smape: 111.0792\n",
      "Epoch 25/40\n",
      "13s - loss: 0.9512 - k_smape: 110.0645 - val_loss: 0.9701 - val_k_smape: 113.8234\n",
      "Epoch 26/40\n",
      "13s - loss: 0.9475 - k_smape: 109.6339 - val_loss: 0.9660 - val_k_smape: 111.6492\n",
      "Epoch 27/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13s - loss: 0.9460 - k_smape: 109.6420 - val_loss: 0.9663 - val_k_smape: 110.8395\n",
      "Epoch 28/40\n",
      "13s - loss: 0.9447 - k_smape: 109.4746 - val_loss: 0.9709 - val_k_smape: 113.8192\n",
      "Epoch 29/40\n",
      "13s - loss: 0.9419 - k_smape: 109.3429 - val_loss: 0.9643 - val_k_smape: 111.4181\n",
      "Epoch 30/40\n",
      "13s - loss: 0.9395 - k_smape: 109.0520 - val_loss: 0.9607 - val_k_smape: 111.2516\n",
      "Epoch 31/40\n",
      "13s - loss: 0.9375 - k_smape: 109.0189 - val_loss: 0.9626 - val_k_smape: 110.9761\n",
      "Epoch 32/40\n",
      "13s - loss: 0.9354 - k_smape: 108.6402 - val_loss: 0.9623 - val_k_smape: 110.2606\n",
      "Epoch 33/40\n",
      "13s - loss: 0.9348 - k_smape: 108.6569 - val_loss: 0.9619 - val_k_smape: 110.9266\n",
      "Epoch 34/40\n",
      "13s - loss: 0.9346 - k_smape: 108.6273 - val_loss: 0.9618 - val_k_smape: 110.6752\n",
      "Epoch 35/40\n",
      "13s - loss: 0.9319 - k_smape: 108.4298 - val_loss: 0.9638 - val_k_smape: 111.4079\n",
      "Epoch 36/40\n",
      "13s - loss: 0.9298 - k_smape: 108.3420 - val_loss: 0.9637 - val_k_smape: 111.5577\n",
      "Epoch 37/40\n",
      "13s - loss: 0.9291 - k_smape: 108.2707 - val_loss: 0.9618 - val_k_smape: 110.2911\n",
      "Epoch 38/40\n",
      "13s - loss: 0.9267 - k_smape: 107.9566 - val_loss: 0.9577 - val_k_smape: 110.9452\n",
      "Epoch 39/40\n",
      "13s - loss: 0.9261 - k_smape: 108.0023 - val_loss: 0.9613 - val_k_smape: 110.6468\n",
      "Epoch 40/40\n",
      "13s - loss: 0.9240 - k_smape: 107.8333 - val_loss: 0.9620 - val_k_smape: 111.5175\n",
      "--- Group: 4 / 4 ---\n",
      "Train on 635 samples, validate on 159 samples\n",
      "Epoch 1/60\n",
      "0s - loss: 1.2752 - k_smape: 170.0642 - val_loss: 1.1878 - val_k_smape: 171.3968\n",
      "Epoch 2/60\n",
      "0s - loss: 1.2585 - k_smape: 162.6977 - val_loss: 1.1725 - val_k_smape: 165.3685\n",
      "Epoch 3/60\n",
      "0s - loss: 1.2469 - k_smape: 156.6599 - val_loss: 1.1565 - val_k_smape: 158.7031\n",
      "Epoch 4/60\n",
      "0s - loss: 1.2268 - k_smape: 151.7629 - val_loss: 1.1390 - val_k_smape: 154.4130\n",
      "Epoch 5/60\n",
      "0s - loss: 1.2105 - k_smape: 146.3607 - val_loss: 1.1176 - val_k_smape: 147.3924\n",
      "Epoch 6/60\n",
      "0s - loss: 1.1919 - k_smape: 142.4035 - val_loss: 1.0994 - val_k_smape: 143.2836\n",
      "Epoch 7/60\n",
      "0s - loss: 1.1617 - k_smape: 136.2383 - val_loss: 1.0745 - val_k_smape: 136.7084\n",
      "Epoch 8/60\n",
      "0s - loss: 1.1457 - k_smape: 131.9790 - val_loss: 1.0587 - val_k_smape: 134.2305\n",
      "Epoch 9/60\n",
      "0s - loss: 1.1294 - k_smape: 129.1917 - val_loss: 1.0411 - val_k_smape: 130.5429\n",
      "Epoch 10/60\n",
      "0s - loss: 1.1156 - k_smape: 128.5855 - val_loss: 1.0323 - val_k_smape: 130.5458\n",
      "Epoch 11/60\n",
      "0s - loss: 1.0897 - k_smape: 124.1860 - val_loss: 1.0075 - val_k_smape: 125.3768\n",
      "Epoch 12/60\n",
      "0s - loss: 1.0726 - k_smape: 121.2738 - val_loss: 0.9969 - val_k_smape: 123.2517\n",
      "Epoch 13/60\n",
      "0s - loss: 1.0512 - k_smape: 117.9858 - val_loss: 0.9996 - val_k_smape: 126.3374\n",
      "Epoch 14/60\n",
      "0s - loss: 1.0414 - k_smape: 116.8772 - val_loss: 0.9704 - val_k_smape: 118.5954\n",
      "Epoch 15/60\n",
      "0s - loss: 1.0299 - k_smape: 115.2810 - val_loss: 0.9724 - val_k_smape: 120.3341\n",
      "Epoch 16/60\n",
      "0s - loss: 1.0067 - k_smape: 112.2850 - val_loss: 0.9631 - val_k_smape: 119.1171\n",
      "Epoch 17/60\n",
      "0s - loss: 0.9969 - k_smape: 110.7409 - val_loss: 0.9510 - val_k_smape: 116.9875\n",
      "Epoch 18/60\n",
      "0s - loss: 0.9868 - k_smape: 110.2387 - val_loss: 0.9484 - val_k_smape: 115.4352\n",
      "Epoch 19/60\n",
      "0s - loss: 0.9776 - k_smape: 110.1874 - val_loss: 0.9410 - val_k_smape: 114.0479\n",
      "Epoch 20/60\n",
      "0s - loss: 0.9631 - k_smape: 106.7566 - val_loss: 0.9401 - val_k_smape: 116.0376\n",
      "Epoch 21/60\n",
      "0s - loss: 0.9568 - k_smape: 107.8847 - val_loss: 0.9270 - val_k_smape: 113.0956\n",
      "Epoch 22/60\n",
      "0s - loss: 0.9437 - k_smape: 105.7472 - val_loss: 0.9315 - val_k_smape: 113.4104\n",
      "Epoch 23/60\n",
      "0s - loss: 0.9324 - k_smape: 104.9973 - val_loss: 0.9260 - val_k_smape: 112.3904\n",
      "Epoch 24/60\n",
      "0s - loss: 0.9275 - k_smape: 104.8154 - val_loss: 0.9228 - val_k_smape: 112.1914\n",
      "Epoch 25/60\n",
      "0s - loss: 0.9131 - k_smape: 102.1870 - val_loss: 0.9097 - val_k_smape: 109.8995\n",
      "Epoch 26/60\n",
      "0s - loss: 0.9187 - k_smape: 103.0447 - val_loss: 0.9146 - val_k_smape: 108.9276\n",
      "Epoch 27/60\n",
      "0s - loss: 0.8960 - k_smape: 100.8802 - val_loss: 0.9071 - val_k_smape: 110.4471\n",
      "Epoch 28/60\n",
      "0s - loss: 0.8998 - k_smape: 102.4275 - val_loss: 0.9118 - val_k_smape: 108.7798\n",
      "Epoch 29/60\n",
      "0s - loss: 0.8887 - k_smape: 101.0693 - val_loss: 0.8996 - val_k_smape: 108.5252\n",
      "Epoch 30/60\n",
      "0s - loss: 0.8745 - k_smape: 99.3135 - val_loss: 0.8958 - val_k_smape: 107.7490\n",
      "Epoch 31/60\n",
      "0s - loss: 0.8941 - k_smape: 100.6556 - val_loss: 0.9008 - val_k_smape: 107.6732\n",
      "Epoch 32/60\n",
      "0s - loss: 0.8702 - k_smape: 98.7388 - val_loss: 0.8974 - val_k_smape: 106.2278\n",
      "Epoch 33/60\n",
      "0s - loss: 0.8757 - k_smape: 99.1391 - val_loss: 0.9002 - val_k_smape: 106.6662\n",
      "Epoch 34/60\n",
      "0s - loss: 0.8656 - k_smape: 97.3801 - val_loss: 0.9003 - val_k_smape: 107.6934\n",
      "Epoch 35/60\n",
      "0s - loss: 0.8584 - k_smape: 98.0272 - val_loss: 0.8954 - val_k_smape: 108.0319\n",
      "Epoch 36/60\n",
      "0s - loss: 0.8489 - k_smape: 96.2064 - val_loss: 0.8948 - val_k_smape: 105.7057\n",
      "Epoch 37/60\n",
      "0s - loss: 0.8466 - k_smape: 96.7758 - val_loss: 0.8950 - val_k_smape: 107.3328\n",
      "Epoch 38/60\n",
      "0s - loss: 0.8539 - k_smape: 96.9811 - val_loss: 0.9068 - val_k_smape: 106.8274\n",
      "Epoch 39/60\n",
      "0s - loss: 0.8464 - k_smape: 97.6259 - val_loss: 0.8927 - val_k_smape: 106.9917\n",
      "Epoch 40/60\n",
      "0s - loss: 0.8544 - k_smape: 95.7804 - val_loss: 0.9025 - val_k_smape: 107.2024\n",
      "Epoch 41/60\n",
      "0s - loss: 0.8426 - k_smape: 98.0733 - val_loss: 0.9141 - val_k_smape: 108.1145\n",
      "Epoch 42/60\n",
      "0s - loss: 0.8372 - k_smape: 96.0652 - val_loss: 0.9130 - val_k_smape: 107.4912\n",
      "Epoch 43/60\n",
      "0s - loss: 0.8250 - k_smape: 95.3712 - val_loss: 0.9016 - val_k_smape: 106.7641\n",
      "Epoch 44/60\n",
      "0s - loss: 0.8215 - k_smape: 95.4034 - val_loss: 0.8911 - val_k_smape: 104.6957\n",
      "Epoch 45/60\n",
      "0s - loss: 0.8247 - k_smape: 94.5587 - val_loss: 0.8908 - val_k_smape: 105.2215\n",
      "Epoch 46/60\n",
      "0s - loss: 0.8189 - k_smape: 94.1637 - val_loss: 0.8972 - val_k_smape: 105.2175\n",
      "Epoch 47/60\n",
      "0s - loss: 0.8008 - k_smape: 93.2880 - val_loss: 0.8820 - val_k_smape: 104.3309\n",
      "Epoch 48/60\n",
      "0s - loss: 0.8059 - k_smape: 93.4649 - val_loss: 0.8760 - val_k_smape: 102.9869\n",
      "Epoch 49/60\n",
      "0s - loss: 0.8002 - k_smape: 92.7538 - val_loss: 0.8928 - val_k_smape: 105.4662\n",
      "Epoch 50/60\n",
      "0s - loss: 0.7937 - k_smape: 91.9213 - val_loss: 0.8955 - val_k_smape: 105.1741\n",
      "Epoch 51/60\n",
      "0s - loss: 0.7974 - k_smape: 92.3792 - val_loss: 0.8848 - val_k_smape: 104.5494\n",
      "Epoch 52/60\n",
      "0s - loss: 0.7848 - k_smape: 92.1522 - val_loss: 0.8975 - val_k_smape: 105.4483\n",
      "Epoch 53/60\n",
      "0s - loss: 0.7892 - k_smape: 93.3545 - val_loss: 0.8929 - val_k_smape: 106.5681\n",
      "Epoch 54/60\n",
      "0s - loss: 0.7916 - k_smape: 92.5565 - val_loss: 0.9066 - val_k_smape: 106.5200\n",
      "Epoch 55/60\n",
      "0s - loss: 0.7958 - k_smape: 91.7288 - val_loss: 0.8951 - val_k_smape: 105.9858\n",
      "Epoch 56/60\n",
      "0s - loss: 0.7787 - k_smape: 91.6880 - val_loss: 0.8876 - val_k_smape: 104.9006\n",
      "Epoch 57/60\n",
      "0s - loss: 0.7731 - k_smape: 90.9905 - val_loss: 0.8827 - val_k_smape: 103.7217\n",
      "Epoch 58/60\n",
      "0s - loss: 0.7811 - k_smape: 91.6485 - val_loss: 0.8805 - val_k_smape: 104.7271\n",
      "Epoch 59/60\n",
      "0s - loss: 0.7777 - k_smape: 92.0582 - val_loss: 0.8904 - val_k_smape: 105.9275\n",
      "Epoch 60/60\n",
      "0s - loss: 0.7803 - k_smape: 93.1353 - val_loss: 0.9010 - val_k_smape: 105.0570\n",
      "=== Run: 4 / 5 ===\n",
      "--- Group: 1 / 4 ---\n",
      "Train on 21930 samples, validate on 5483 samples\n",
      "--- Group: 2 / 4 ---\n",
      "Train on 32032 samples, validate on 8009 samples\n",
      "Epoch 1/40\n",
      "7s - loss: 1.0647 - k_smape: 155.9201 - val_loss: 1.0180 - val_k_smape: 151.0625\n",
      "Epoch 2/40\n",
      "7s - loss: 1.0440 - k_smape: 148.4688 - val_loss: 1.0076 - val_k_smape: 145.3982\n",
      "Epoch 3/40\n",
      "7s - loss: 1.0371 - k_smape: 146.6656 - val_loss: 1.0071 - val_k_smape: 146.5378\n",
      "Epoch 4/40\n",
      "7s - loss: 1.0323 - k_smape: 145.4670 - val_loss: 1.0022 - val_k_smape: 144.4365\n",
      "Epoch 5/40\n",
      "7s - loss: 1.0278 - k_smape: 144.3989 - val_loss: 1.0030 - val_k_smape: 146.4284\n",
      "Epoch 6/40\n",
      "7s - loss: 1.0240 - k_smape: 143.7674 - val_loss: 0.9982 - val_k_smape: 142.5249\n",
      "Epoch 7/40\n",
      "7s - loss: 1.0210 - k_smape: 142.8357 - val_loss: 0.9968 - val_k_smape: 141.1425\n",
      "Epoch 8/40\n",
      "7s - loss: 1.0177 - k_smape: 142.3107 - val_loss: 0.9958 - val_k_smape: 143.1273\n",
      "Epoch 9/40\n",
      "7s - loss: 1.0159 - k_smape: 141.7786 - val_loss: 0.9958 - val_k_smape: 142.6628\n",
      "Epoch 10/40\n",
      "7s - loss: 1.0124 - k_smape: 141.4055 - val_loss: 0.9960 - val_k_smape: 140.7587\n",
      "Epoch 11/40\n",
      "7s - loss: 1.0092 - k_smape: 140.6082 - val_loss: 0.9968 - val_k_smape: 143.5403\n",
      "Epoch 12/40\n",
      "7s - loss: 1.0071 - k_smape: 140.3613 - val_loss: 0.9942 - val_k_smape: 139.9163\n",
      "Epoch 13/40\n",
      "7s - loss: 1.0048 - k_smape: 139.9158 - val_loss: 0.9974 - val_k_smape: 142.5611\n",
      "Epoch 14/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7s - loss: 1.0034 - k_smape: 139.7223 - val_loss: 0.9970 - val_k_smape: 141.1966\n",
      "Epoch 15/40\n",
      "7s - loss: 0.9982 - k_smape: 138.9774 - val_loss: 0.9948 - val_k_smape: 141.6787\n",
      "Epoch 16/40\n",
      "7s - loss: 0.9973 - k_smape: 138.8907 - val_loss: 0.9949 - val_k_smape: 140.4727\n",
      "Epoch 17/40\n",
      "7s - loss: 0.9933 - k_smape: 138.3203 - val_loss: 0.9966 - val_k_smape: 141.8183\n",
      "Epoch 18/40\n",
      "7s - loss: 0.9923 - k_smape: 138.2967 - val_loss: 0.9956 - val_k_smape: 140.3979\n",
      "Epoch 19/40\n",
      "7s - loss: 0.9901 - k_smape: 137.8365 - val_loss: 0.9966 - val_k_smape: 142.0865\n",
      "Epoch 20/40\n",
      "7s - loss: 0.9871 - k_smape: 137.7258 - val_loss: 0.9985 - val_k_smape: 142.2365\n",
      "Epoch 21/40\n",
      "7s - loss: 0.9864 - k_smape: 137.6827 - val_loss: 0.9994 - val_k_smape: 140.9391\n",
      "Epoch 22/40\n",
      "7s - loss: 0.9814 - k_smape: 137.2060 - val_loss: 1.0044 - val_k_smape: 144.7240\n",
      "Epoch 23/40\n",
      "7s - loss: 0.9821 - k_smape: 137.1696 - val_loss: 0.9970 - val_k_smape: 141.0350\n",
      "Epoch 24/40\n",
      "7s - loss: 0.9793 - k_smape: 136.9424 - val_loss: 0.9958 - val_k_smape: 140.3975\n",
      "Epoch 25/40\n",
      "7s - loss: 0.9758 - k_smape: 136.9013 - val_loss: 1.0008 - val_k_smape: 142.4373\n",
      "Epoch 26/40\n",
      "7s - loss: 0.9741 - k_smape: 136.4899 - val_loss: 0.9964 - val_k_smape: 141.4576\n",
      "Epoch 27/40\n",
      "7s - loss: 0.9724 - k_smape: 136.3507 - val_loss: 0.9948 - val_k_smape: 140.0347\n",
      "Epoch 28/40\n",
      "7s - loss: 0.9704 - k_smape: 136.0764 - val_loss: 0.9969 - val_k_smape: 140.9922\n",
      "Epoch 29/40\n",
      "7s - loss: 0.9693 - k_smape: 136.0749 - val_loss: 0.9978 - val_k_smape: 141.5828\n",
      "Epoch 30/40\n",
      "7s - loss: 0.9655 - k_smape: 135.8153 - val_loss: 0.9958 - val_k_smape: 140.0513\n",
      "Epoch 31/40\n",
      "7s - loss: 0.9647 - k_smape: 135.7691 - val_loss: 0.9987 - val_k_smape: 141.5496\n",
      "Epoch 32/40\n",
      "7s - loss: 0.9662 - k_smape: 135.9421 - val_loss: 1.0012 - val_k_smape: 140.8670\n",
      "Epoch 33/40\n",
      "7s - loss: 0.9595 - k_smape: 135.1608 - val_loss: 0.9987 - val_k_smape: 142.1321\n",
      "Epoch 34/40\n",
      "7s - loss: 0.9599 - k_smape: 135.4142 - val_loss: 1.0002 - val_k_smape: 141.8617\n",
      "Epoch 35/40\n",
      "7s - loss: 0.9594 - k_smape: 135.1277 - val_loss: 1.0011 - val_k_smape: 141.1056\n",
      "Epoch 36/40\n",
      "7s - loss: 0.9556 - k_smape: 134.9320 - val_loss: 0.9979 - val_k_smape: 141.2265\n",
      "Epoch 37/40\n",
      "7s - loss: 0.9544 - k_smape: 134.8218 - val_loss: 1.0043 - val_k_smape: 141.5968\n",
      "Epoch 38/40\n",
      "7s - loss: 0.9543 - k_smape: 134.9579 - val_loss: 1.0022 - val_k_smape: 141.5718\n",
      "Epoch 39/40\n",
      "7s - loss: 0.9531 - k_smape: 134.6330 - val_loss: 1.0029 - val_k_smape: 139.5228\n",
      "Epoch 40/40\n",
      "7s - loss: 0.9526 - k_smape: 134.7355 - val_loss: 1.0038 - val_k_smape: 141.1792\n",
      "--- Group: 3 / 4 ---\n",
      "Train on 61452 samples, validate on 15363 samples\n",
      "Epoch 1/40\n",
      "14s - loss: 1.0904 - k_smape: 128.5367 - val_loss: 1.0422 - val_k_smape: 122.5692\n",
      "Epoch 2/40\n",
      "13s - loss: 1.0496 - k_smape: 120.9959 - val_loss: 1.0195 - val_k_smape: 117.8499\n",
      "Epoch 3/40\n",
      "13s - loss: 1.0363 - k_smape: 119.0524 - val_loss: 1.0094 - val_k_smape: 116.4607\n",
      "Epoch 4/40\n",
      "13s - loss: 1.0279 - k_smape: 117.8913 - val_loss: 1.0025 - val_k_smape: 113.6142\n",
      "Epoch 5/40\n",
      "13s - loss: 1.0220 - k_smape: 117.0647 - val_loss: 1.0027 - val_k_smape: 114.6368\n",
      "Epoch 6/40\n",
      "13s - loss: 1.0171 - k_smape: 116.5987 - val_loss: 0.9971 - val_k_smape: 115.6711\n",
      "Epoch 7/40\n",
      "13s - loss: 1.0121 - k_smape: 115.9547 - val_loss: 0.9997 - val_k_smape: 117.5538\n",
      "Epoch 8/40\n",
      "13s - loss: 1.0081 - k_smape: 115.4568 - val_loss: 0.9965 - val_k_smape: 115.2766\n",
      "Epoch 9/40\n",
      "13s - loss: 1.0028 - k_smape: 115.0612 - val_loss: 0.9896 - val_k_smape: 113.9782\n",
      "Epoch 10/40\n",
      "13s - loss: 0.9987 - k_smape: 114.4261 - val_loss: 0.9894 - val_k_smape: 114.2253\n",
      "Epoch 11/40\n",
      "13s - loss: 0.9953 - k_smape: 113.9145 - val_loss: 0.9827 - val_k_smape: 112.8365\n",
      "Epoch 12/40\n",
      "13s - loss: 0.9922 - k_smape: 113.9418 - val_loss: 0.9827 - val_k_smape: 112.2031\n",
      "Epoch 13/40\n",
      "13s - loss: 0.9901 - k_smape: 113.5060 - val_loss: 0.9824 - val_k_smape: 113.4517\n",
      "Epoch 14/40\n",
      "13s - loss: 0.9864 - k_smape: 113.2136 - val_loss: 0.9816 - val_k_smape: 114.2351\n",
      "Epoch 15/40\n",
      "13s - loss: 0.9822 - k_smape: 112.7972 - val_loss: 0.9775 - val_k_smape: 110.4466\n",
      "Epoch 16/40\n",
      "13s - loss: 0.9799 - k_smape: 112.6481 - val_loss: 0.9769 - val_k_smape: 113.2449\n",
      "Epoch 17/40\n",
      "13s - loss: 0.9766 - k_smape: 112.3819 - val_loss: 0.9798 - val_k_smape: 112.3013\n",
      "Epoch 18/40\n",
      "13s - loss: 0.9741 - k_smape: 111.9948 - val_loss: 0.9782 - val_k_smape: 113.2318\n",
      "Epoch 19/40\n",
      "13s - loss: 0.9710 - k_smape: 111.8062 - val_loss: 0.9733 - val_k_smape: 112.0790\n",
      "Epoch 20/40\n",
      "13s - loss: 0.9671 - k_smape: 111.4852 - val_loss: 0.9748 - val_k_smape: 111.8278\n",
      "Epoch 21/40\n",
      "13s - loss: 0.9667 - k_smape: 111.4366 - val_loss: 0.9761 - val_k_smape: 113.3057\n",
      "Epoch 22/40\n",
      "13s - loss: 0.9646 - k_smape: 111.2963 - val_loss: 0.9747 - val_k_smape: 113.2805\n",
      "Epoch 23/40\n",
      "13s - loss: 0.9610 - k_smape: 110.9223 - val_loss: 0.9738 - val_k_smape: 112.1431\n",
      "Epoch 24/40\n",
      "13s - loss: 0.9594 - k_smape: 110.8671 - val_loss: 0.9727 - val_k_smape: 111.9734\n",
      "Epoch 25/40\n",
      "13s - loss: 0.9566 - k_smape: 110.5679 - val_loss: 0.9767 - val_k_smape: 113.5886\n",
      "Epoch 26/40\n",
      "13s - loss: 0.9533 - k_smape: 110.3387 - val_loss: 0.9753 - val_k_smape: 114.0999\n",
      "Epoch 27/40\n",
      "13s - loss: 0.9527 - k_smape: 110.2394 - val_loss: 0.9690 - val_k_smape: 110.2197\n",
      "Epoch 28/40\n",
      "13s - loss: 0.9520 - k_smape: 110.2059 - val_loss: 0.9638 - val_k_smape: 110.7193\n",
      "Epoch 29/40\n",
      "13s - loss: 0.9495 - k_smape: 110.0386 - val_loss: 0.9720 - val_k_smape: 113.0088\n",
      "Epoch 30/40\n",
      "13s - loss: 0.9472 - k_smape: 109.7440 - val_loss: 0.9701 - val_k_smape: 112.4047\n",
      "Epoch 31/40\n",
      "13s - loss: 0.9454 - k_smape: 109.6387 - val_loss: 0.9645 - val_k_smape: 111.6097\n",
      "Epoch 32/40\n",
      "13s - loss: 0.9428 - k_smape: 109.5086 - val_loss: 0.9684 - val_k_smape: 111.4198\n",
      "Epoch 33/40\n",
      "13s - loss: 0.9426 - k_smape: 109.4433 - val_loss: 0.9659 - val_k_smape: 112.8163\n",
      "Epoch 34/40\n",
      "13s - loss: 0.9398 - k_smape: 109.2582 - val_loss: 0.9696 - val_k_smape: 112.5056\n",
      "Epoch 35/40\n",
      "13s - loss: 0.9394 - k_smape: 109.1951 - val_loss: 0.9671 - val_k_smape: 111.7268\n",
      "Epoch 36/40\n",
      "13s - loss: 0.9361 - k_smape: 108.9285 - val_loss: 0.9666 - val_k_smape: 111.9434\n",
      "Epoch 37/40\n",
      "13s - loss: 0.9376 - k_smape: 109.0705 - val_loss: 0.9697 - val_k_smape: 112.4938\n",
      "Epoch 38/40\n",
      "13s - loss: 0.9332 - k_smape: 108.6960 - val_loss: 0.9678 - val_k_smape: 112.7706\n",
      "Epoch 39/40\n",
      "13s - loss: 0.9322 - k_smape: 108.6164 - val_loss: 0.9640 - val_k_smape: 112.0976\n",
      "Epoch 40/40\n",
      "13s - loss: 0.9309 - k_smape: 108.4660 - val_loss: 0.9637 - val_k_smape: 111.3452\n",
      "--- Group: 4 / 4 ---\n",
      "Train on 635 samples, validate on 159 samples\n",
      "Epoch 1/60\n",
      "0s - loss: 1.2547 - k_smape: 174.1346 - val_loss: 1.2885 - val_k_smape: 176.4177\n",
      "Epoch 2/60\n",
      "0s - loss: 1.2416 - k_smape: 165.8053 - val_loss: 1.2697 - val_k_smape: 167.1187\n",
      "Epoch 3/60\n",
      "0s - loss: 1.2255 - k_smape: 161.1311 - val_loss: 1.2569 - val_k_smape: 162.6705\n",
      "Epoch 4/60\n",
      "0s - loss: 1.2101 - k_smape: 154.4282 - val_loss: 1.2347 - val_k_smape: 154.5284\n",
      "Epoch 5/60\n",
      "0s - loss: 1.1966 - k_smape: 148.4432 - val_loss: 1.2129 - val_k_smape: 148.0959\n",
      "Epoch 6/60\n",
      "0s - loss: 1.1768 - k_smape: 144.7131 - val_loss: 1.1930 - val_k_smape: 144.2130\n",
      "Epoch 7/60\n",
      "0s - loss: 1.1539 - k_smape: 139.5918 - val_loss: 1.1676 - val_k_smape: 137.5863\n",
      "Epoch 8/60\n",
      "0s - loss: 1.1427 - k_smape: 135.2519 - val_loss: 1.1546 - val_k_smape: 136.7205\n",
      "Epoch 9/60\n",
      "0s - loss: 1.1200 - k_smape: 133.9204 - val_loss: 1.1273 - val_k_smape: 128.8183\n",
      "Epoch 10/60\n",
      "0s - loss: 1.1040 - k_smape: 130.0376 - val_loss: 1.1229 - val_k_smape: 132.3096\n",
      "Epoch 11/60\n",
      "0s - loss: 1.0854 - k_smape: 127.0091 - val_loss: 1.0954 - val_k_smape: 124.3914\n",
      "Epoch 12/60\n",
      "0s - loss: 1.0770 - k_smape: 126.3659 - val_loss: 1.0951 - val_k_smape: 127.4067\n",
      "Epoch 13/60\n",
      "0s - loss: 1.0656 - k_smape: 123.0403 - val_loss: 1.0621 - val_k_smape: 120.7797\n",
      "Epoch 14/60\n",
      "0s - loss: 1.0455 - k_smape: 121.3862 - val_loss: 1.0613 - val_k_smape: 120.7825\n",
      "Epoch 15/60\n",
      "0s - loss: 1.0380 - k_smape: 118.9957 - val_loss: 1.0425 - val_k_smape: 116.3750\n",
      "Epoch 16/60\n",
      "0s - loss: 1.0230 - k_smape: 117.5489 - val_loss: 1.0399 - val_k_smape: 118.6480\n",
      "Epoch 17/60\n",
      "0s - loss: 1.0066 - k_smape: 114.6551 - val_loss: 1.0318 - val_k_smape: 115.7721\n",
      "Epoch 18/60\n",
      "0s - loss: 0.9950 - k_smape: 114.5672 - val_loss: 1.0158 - val_k_smape: 114.3917\n",
      "Epoch 19/60\n",
      "0s - loss: 0.9775 - k_smape: 111.6839 - val_loss: 1.0128 - val_k_smape: 114.7103\n",
      "Epoch 20/60\n",
      "0s - loss: 0.9705 - k_smape: 111.8405 - val_loss: 0.9954 - val_k_smape: 110.8001\n",
      "Epoch 21/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s - loss: 0.9611 - k_smape: 109.2611 - val_loss: 1.0025 - val_k_smape: 112.8988\n",
      "Epoch 22/60\n",
      "0s - loss: 0.9459 - k_smape: 107.8876 - val_loss: 0.9788 - val_k_smape: 110.0052\n",
      "Epoch 23/60\n",
      "0s - loss: 0.9467 - k_smape: 109.5414 - val_loss: 0.9675 - val_k_smape: 108.1349\n",
      "Epoch 24/60\n",
      "0s - loss: 0.9267 - k_smape: 107.3545 - val_loss: 0.9720 - val_k_smape: 109.0768\n",
      "Epoch 25/60\n",
      "0s - loss: 0.9311 - k_smape: 105.8893 - val_loss: 0.9790 - val_k_smape: 110.9287\n",
      "Epoch 26/60\n",
      "0s - loss: 0.9154 - k_smape: 105.7897 - val_loss: 0.9639 - val_k_smape: 106.7785\n",
      "Epoch 27/60\n",
      "0s - loss: 0.9129 - k_smape: 105.0712 - val_loss: 0.9610 - val_k_smape: 107.7873\n",
      "Epoch 28/60\n",
      "0s - loss: 0.9016 - k_smape: 104.2845 - val_loss: 0.9455 - val_k_smape: 106.1810\n",
      "Epoch 29/60\n",
      "0s - loss: 0.8985 - k_smape: 104.1396 - val_loss: 0.9708 - val_k_smape: 108.8029\n",
      "Epoch 30/60\n",
      "0s - loss: 0.9077 - k_smape: 105.2652 - val_loss: 0.9472 - val_k_smape: 105.9798\n",
      "Epoch 31/60\n",
      "0s - loss: 0.8870 - k_smape: 103.2075 - val_loss: 0.9462 - val_k_smape: 105.7461\n",
      "Epoch 32/60\n",
      "0s - loss: 0.8754 - k_smape: 101.7965 - val_loss: 0.9412 - val_k_smape: 105.9735\n",
      "Epoch 33/60\n",
      "0s - loss: 0.8676 - k_smape: 100.9411 - val_loss: 0.9297 - val_k_smape: 103.1497\n",
      "Epoch 34/60\n",
      "0s - loss: 0.8710 - k_smape: 101.3185 - val_loss: 0.9357 - val_k_smape: 106.4430\n",
      "Epoch 35/60\n",
      "0s - loss: 0.8563 - k_smape: 101.9689 - val_loss: 0.9228 - val_k_smape: 102.4330\n",
      "Epoch 36/60\n",
      "0s - loss: 0.8665 - k_smape: 99.9889 - val_loss: 0.9270 - val_k_smape: 103.3271\n",
      "Epoch 37/60\n",
      "0s - loss: 0.8460 - k_smape: 100.3923 - val_loss: 0.9356 - val_k_smape: 104.3100\n",
      "Epoch 38/60\n",
      "0s - loss: 0.8603 - k_smape: 99.3268 - val_loss: 0.9465 - val_k_smape: 106.3336\n",
      "Epoch 39/60\n",
      "0s - loss: 0.8515 - k_smape: 101.2338 - val_loss: 0.9327 - val_k_smape: 102.4413\n",
      "Epoch 40/60\n",
      "0s - loss: 0.8612 - k_smape: 99.0144 - val_loss: 0.9423 - val_k_smape: 106.5885\n",
      "Epoch 41/60\n",
      "0s - loss: 0.8513 - k_smape: 100.7716 - val_loss: 0.9206 - val_k_smape: 100.7461\n",
      "Epoch 42/60\n",
      "0s - loss: 0.8594 - k_smape: 99.3947 - val_loss: 0.9180 - val_k_smape: 103.5193\n",
      "Epoch 43/60\n",
      "0s - loss: 0.8369 - k_smape: 98.4626 - val_loss: 0.9193 - val_k_smape: 102.5950\n",
      "Epoch 44/60\n",
      "0s - loss: 0.8350 - k_smape: 98.5102 - val_loss: 0.9056 - val_k_smape: 101.3226\n",
      "Epoch 45/60\n",
      "0s - loss: 0.8357 - k_smape: 97.7173 - val_loss: 0.9006 - val_k_smape: 99.8724\n",
      "Epoch 46/60\n",
      "0s - loss: 0.8213 - k_smape: 96.7710 - val_loss: 0.9059 - val_k_smape: 101.0165\n",
      "Epoch 47/60\n",
      "0s - loss: 0.8163 - k_smape: 95.7712 - val_loss: 0.9351 - val_k_smape: 103.2990\n",
      "Epoch 48/60\n",
      "0s - loss: 0.8248 - k_smape: 96.7752 - val_loss: 0.9121 - val_k_smape: 100.5193\n",
      "Epoch 49/60\n",
      "0s - loss: 0.8172 - k_smape: 95.5809 - val_loss: 0.9108 - val_k_smape: 100.8568\n",
      "Epoch 50/60\n",
      "0s - loss: 0.8188 - k_smape: 97.0713 - val_loss: 0.9153 - val_k_smape: 101.4463\n",
      "Epoch 51/60\n",
      "0s - loss: 0.8188 - k_smape: 97.2121 - val_loss: 0.9121 - val_k_smape: 102.1804\n",
      "Epoch 52/60\n",
      "0s - loss: 0.8129 - k_smape: 95.9340 - val_loss: 0.9120 - val_k_smape: 101.0241\n",
      "Epoch 53/60\n",
      "0s - loss: 0.8089 - k_smape: 96.6378 - val_loss: 0.9140 - val_k_smape: 100.7184\n",
      "Epoch 54/60\n",
      "0s - loss: 0.8104 - k_smape: 95.2263 - val_loss: 0.8977 - val_k_smape: 99.7119\n",
      "Epoch 55/60\n",
      "0s - loss: 0.7917 - k_smape: 94.4898 - val_loss: 0.8949 - val_k_smape: 99.9385\n",
      "Epoch 56/60\n",
      "0s - loss: 0.8093 - k_smape: 96.1323 - val_loss: 0.8888 - val_k_smape: 97.8148\n",
      "Epoch 57/60\n",
      "0s - loss: 0.8097 - k_smape: 93.3776 - val_loss: 0.8975 - val_k_smape: 100.0161\n",
      "Epoch 58/60\n",
      "0s - loss: 0.7895 - k_smape: 93.7692 - val_loss: 0.8962 - val_k_smape: 99.7982\n",
      "Epoch 59/60\n",
      "0s - loss: 0.7932 - k_smape: 94.3763 - val_loss: 0.8857 - val_k_smape: 98.5302\n",
      "Epoch 60/60\n",
      "0s - loss: 0.7931 - k_smape: 94.1028 - val_loss: 0.8943 - val_k_smape: 98.5551\n",
      "=== Run: 5 / 5 ===\n",
      "--- Group: 1 / 4 ---\n",
      "Train on 21930 samples, validate on 5483 samples\n",
      "--- Group: 2 / 4 ---\n",
      "Train on 32032 samples, validate on 8009 samples\n",
      "Epoch 1/40\n",
      "7s - loss: 1.0584 - k_smape: 156.9277 - val_loss: 1.0504 - val_k_smape: 151.3321\n",
      "Epoch 2/40\n",
      "7s - loss: 1.0375 - k_smape: 148.9319 - val_loss: 1.0446 - val_k_smape: 148.1965\n",
      "Epoch 3/40\n",
      "7s - loss: 1.0310 - k_smape: 147.0496 - val_loss: 1.0407 - val_k_smape: 145.4617\n",
      "Epoch 4/40\n",
      "7s - loss: 1.0267 - k_smape: 145.5966 - val_loss: 1.0376 - val_k_smape: 146.3040\n",
      "Epoch 5/40\n",
      "7s - loss: 1.0218 - k_smape: 144.9778 - val_loss: 1.0363 - val_k_smape: 145.4281\n",
      "Epoch 6/40\n",
      "7s - loss: 1.0186 - k_smape: 143.9065 - val_loss: 1.0359 - val_k_smape: 146.2001\n",
      "Epoch 7/40\n",
      "7s - loss: 1.0155 - k_smape: 143.1419 - val_loss: 1.0314 - val_k_smape: 143.2916\n",
      "Epoch 8/40\n",
      "7s - loss: 1.0138 - k_smape: 142.5353 - val_loss: 1.0308 - val_k_smape: 142.9251\n",
      "Epoch 9/40\n",
      "7s - loss: 1.0104 - k_smape: 142.0042 - val_loss: 1.0278 - val_k_smape: 141.5966\n",
      "Epoch 10/40\n",
      "7s - loss: 1.0073 - k_smape: 141.3337 - val_loss: 1.0293 - val_k_smape: 143.4716\n",
      "Epoch 11/40\n",
      "7s - loss: 1.0065 - k_smape: 141.3071 - val_loss: 1.0279 - val_k_smape: 143.6745\n",
      "Epoch 12/40\n",
      "7s - loss: 1.0043 - k_smape: 141.0037 - val_loss: 1.0264 - val_k_smape: 142.8743\n",
      "Epoch 13/40\n",
      "7s - loss: 1.0014 - k_smape: 140.4442 - val_loss: 1.0308 - val_k_smape: 144.9197\n",
      "Epoch 14/40\n",
      "7s - loss: 0.9993 - k_smape: 140.1550 - val_loss: 1.0251 - val_k_smape: 142.5837\n",
      "Epoch 15/40\n",
      "7s - loss: 0.9980 - k_smape: 139.9134 - val_loss: 1.0263 - val_k_smape: 143.0031\n",
      "Epoch 16/40\n",
      "7s - loss: 0.9953 - k_smape: 139.5467 - val_loss: 1.0250 - val_k_smape: 142.6168\n",
      "Epoch 17/40\n",
      "7s - loss: 0.9938 - k_smape: 139.4697 - val_loss: 1.0251 - val_k_smape: 143.4049\n",
      "Epoch 18/40\n",
      "7s - loss: 0.9903 - k_smape: 139.1668 - val_loss: 1.0233 - val_k_smape: 141.9901\n",
      "Epoch 19/40\n",
      "7s - loss: 0.9879 - k_smape: 138.6681 - val_loss: 1.0242 - val_k_smape: 140.0334\n",
      "Epoch 20/40\n",
      "7s - loss: 0.9855 - k_smape: 138.5594 - val_loss: 1.0253 - val_k_smape: 142.7155\n",
      "Epoch 21/40\n",
      "7s - loss: 0.9830 - k_smape: 138.1818 - val_loss: 1.0247 - val_k_smape: 141.7232\n",
      "Epoch 22/40\n",
      "7s - loss: 0.9839 - k_smape: 138.3027 - val_loss: 1.0249 - val_k_smape: 141.2622\n",
      "Epoch 23/40\n",
      "7s - loss: 0.9801 - k_smape: 137.9679 - val_loss: 1.0239 - val_k_smape: 141.0976\n",
      "Epoch 24/40\n",
      "7s - loss: 0.9790 - k_smape: 137.8241 - val_loss: 1.0248 - val_k_smape: 142.0833\n",
      "Epoch 25/40\n",
      "7s - loss: 0.9769 - k_smape: 137.5301 - val_loss: 1.0253 - val_k_smape: 141.7817\n",
      "Epoch 26/40\n",
      "7s - loss: 0.9752 - k_smape: 137.3061 - val_loss: 1.0266 - val_k_smape: 140.0606\n",
      "Epoch 27/40\n",
      "7s - loss: 0.9731 - k_smape: 137.3442 - val_loss: 1.0302 - val_k_smape: 143.4200\n",
      "Epoch 28/40\n",
      "7s - loss: 0.9715 - k_smape: 137.3643 - val_loss: 1.0288 - val_k_smape: 140.6951\n",
      "Epoch 29/40\n",
      "7s - loss: 0.9698 - k_smape: 136.9797 - val_loss: 1.0256 - val_k_smape: 141.2445\n",
      "Epoch 30/40\n",
      "7s - loss: 0.9688 - k_smape: 136.9535 - val_loss: 1.0259 - val_k_smape: 139.9841\n",
      "Epoch 31/40\n",
      "7s - loss: 0.9672 - k_smape: 136.9049 - val_loss: 1.0267 - val_k_smape: 141.3611\n",
      "Epoch 32/40\n",
      "7s - loss: 0.9654 - k_smape: 136.8461 - val_loss: 1.0273 - val_k_smape: 141.8014\n",
      "Epoch 33/40\n",
      "7s - loss: 0.9644 - k_smape: 136.6179 - val_loss: 1.0270 - val_k_smape: 140.9037\n",
      "Epoch 34/40\n",
      "7s - loss: 0.9624 - k_smape: 136.2594 - val_loss: 1.0262 - val_k_smape: 142.8641\n",
      "Epoch 35/40\n",
      "7s - loss: 0.9606 - k_smape: 136.2155 - val_loss: 1.0280 - val_k_smape: 143.2548\n",
      "Epoch 36/40\n",
      "7s - loss: 0.9575 - k_smape: 136.0391 - val_loss: 1.0296 - val_k_smape: 140.8070\n",
      "Epoch 37/40\n",
      "7s - loss: 0.9591 - k_smape: 136.2048 - val_loss: 1.0287 - val_k_smape: 139.8462\n",
      "Epoch 38/40\n",
      "7s - loss: 0.9583 - k_smape: 136.2593 - val_loss: 1.0269 - val_k_smape: 141.2658\n",
      "Epoch 39/40\n",
      "7s - loss: 0.9561 - k_smape: 135.7053 - val_loss: 1.0251 - val_k_smape: 141.7730\n",
      "Epoch 40/40\n",
      "7s - loss: 0.9554 - k_smape: 135.6482 - val_loss: 1.0277 - val_k_smape: 142.5074\n",
      "--- Group: 3 / 4 ---\n",
      "Train on 61452 samples, validate on 15363 samples\n",
      "Epoch 1/40\n",
      "14s - loss: 1.0908 - k_smape: 128.8208 - val_loss: 1.0378 - val_k_smape: 119.4808\n",
      "Epoch 2/40\n",
      "13s - loss: 1.0495 - k_smape: 120.9981 - val_loss: 1.0283 - val_k_smape: 119.5775\n",
      "Epoch 3/40\n",
      "13s - loss: 1.0380 - k_smape: 119.3540 - val_loss: 1.0130 - val_k_smape: 115.7748\n",
      "Epoch 4/40\n",
      "13s - loss: 1.0311 - k_smape: 118.3968 - val_loss: 1.0138 - val_k_smape: 117.0633\n",
      "Epoch 5/40\n",
      "13s - loss: 1.0241 - k_smape: 117.4100 - val_loss: 1.0148 - val_k_smape: 118.0961\n",
      "Epoch 6/40\n",
      "13s - loss: 1.0196 - k_smape: 116.8937 - val_loss: 0.9974 - val_k_smape: 113.0797\n",
      "Epoch 7/40\n",
      "13s - loss: 1.0139 - k_smape: 116.0828 - val_loss: 0.9977 - val_k_smape: 114.2771\n",
      "Epoch 8/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13s - loss: 1.0089 - k_smape: 115.6829 - val_loss: 0.9936 - val_k_smape: 113.3086\n",
      "Epoch 9/40\n",
      "13s - loss: 1.0057 - k_smape: 115.1670 - val_loss: 0.9912 - val_k_smape: 112.6280\n",
      "Epoch 10/40\n",
      "13s - loss: 1.0011 - k_smape: 114.6320 - val_loss: 0.9984 - val_k_smape: 113.9089\n",
      "Epoch 11/40\n",
      "13s - loss: 1.0001 - k_smape: 114.3489 - val_loss: 0.9969 - val_k_smape: 115.2248\n",
      "Epoch 12/40\n",
      "13s - loss: 0.9959 - k_smape: 114.0926 - val_loss: 0.9918 - val_k_smape: 114.0429\n",
      "Epoch 13/40\n",
      "13s - loss: 0.9925 - k_smape: 113.7959 - val_loss: 0.9912 - val_k_smape: 113.7644\n",
      "Epoch 14/40\n",
      "13s - loss: 0.9905 - k_smape: 113.5353 - val_loss: 0.9850 - val_k_smape: 113.4797\n",
      "Epoch 15/40\n",
      "13s - loss: 0.9863 - k_smape: 113.0724 - val_loss: 0.9808 - val_k_smape: 112.2486\n",
      "Epoch 16/40\n",
      "13s - loss: 0.9841 - k_smape: 112.8755 - val_loss: 0.9926 - val_k_smape: 115.2899\n",
      "Epoch 17/40\n",
      "13s - loss: 0.9807 - k_smape: 112.6983 - val_loss: 0.9806 - val_k_smape: 111.8776\n",
      "Epoch 18/40\n",
      "13s - loss: 0.9798 - k_smape: 112.5562 - val_loss: 0.9836 - val_k_smape: 111.8058\n",
      "Epoch 19/40\n",
      "13s - loss: 0.9765 - k_smape: 112.2262 - val_loss: 0.9775 - val_k_smape: 111.8374\n",
      "Epoch 20/40\n",
      "13s - loss: 0.9760 - k_smape: 112.1098 - val_loss: 0.9777 - val_k_smape: 112.1053\n",
      "Epoch 21/40\n",
      "13s - loss: 0.9727 - k_smape: 111.7633 - val_loss: 0.9755 - val_k_smape: 111.9581\n",
      "Epoch 22/40\n",
      "13s - loss: 0.9708 - k_smape: 111.8036 - val_loss: 0.9774 - val_k_smape: 111.9543\n",
      "Epoch 23/40\n",
      "13s - loss: 0.9676 - k_smape: 111.5845 - val_loss: 0.9811 - val_k_smape: 113.8017\n",
      "Epoch 24/40\n",
      "13s - loss: 0.9658 - k_smape: 111.2874 - val_loss: 0.9857 - val_k_smape: 114.6776\n",
      "Epoch 25/40\n",
      "13s - loss: 0.9640 - k_smape: 111.1511 - val_loss: 0.9749 - val_k_smape: 110.6706\n",
      "Epoch 26/40\n",
      "13s - loss: 0.9630 - k_smape: 110.9693 - val_loss: 0.9749 - val_k_smape: 112.0285\n",
      "Epoch 27/40\n",
      "13s - loss: 0.9604 - k_smape: 110.7676 - val_loss: 0.9736 - val_k_smape: 111.7186\n",
      "Epoch 28/40\n",
      "13s - loss: 0.9592 - k_smape: 110.7474 - val_loss: 0.9734 - val_k_smape: 111.6409\n",
      "Epoch 29/40\n",
      "13s - loss: 0.9557 - k_smape: 110.4429 - val_loss: 0.9762 - val_k_smape: 112.8811\n",
      "Epoch 30/40\n",
      "13s - loss: 0.9535 - k_smape: 110.3125 - val_loss: 0.9770 - val_k_smape: 111.7598\n",
      "Epoch 31/40\n",
      "13s - loss: 0.9524 - k_smape: 110.0603 - val_loss: 0.9726 - val_k_smape: 111.7850\n",
      "Epoch 32/40\n",
      "13s - loss: 0.9510 - k_smape: 110.1315 - val_loss: 0.9731 - val_k_smape: 111.8502\n",
      "Epoch 33/40\n",
      "13s - loss: 0.9486 - k_smape: 109.9689 - val_loss: 0.9719 - val_k_smape: 110.6323\n",
      "Epoch 34/40\n",
      "13s - loss: 0.9472 - k_smape: 109.8729 - val_loss: 0.9743 - val_k_smape: 112.7629\n",
      "Epoch 35/40\n",
      "13s - loss: 0.9464 - k_smape: 109.7444 - val_loss: 0.9706 - val_k_smape: 111.0371\n",
      "Epoch 36/40\n",
      "13s - loss: 0.9431 - k_smape: 109.3601 - val_loss: 0.9727 - val_k_smape: 110.2872\n",
      "Epoch 37/40\n",
      "13s - loss: 0.9441 - k_smape: 109.5134 - val_loss: 0.9703 - val_k_smape: 112.1124\n",
      "Epoch 38/40\n",
      "13s - loss: 0.9409 - k_smape: 109.3748 - val_loss: 0.9714 - val_k_smape: 111.8685\n",
      "Epoch 39/40\n",
      "13s - loss: 0.9392 - k_smape: 109.2339 - val_loss: 0.9663 - val_k_smape: 109.9824\n",
      "Epoch 40/40\n",
      "13s - loss: 0.9381 - k_smape: 108.9384 - val_loss: 0.9680 - val_k_smape: 111.7153\n",
      "--- Group: 4 / 4 ---\n",
      "Train on 635 samples, validate on 159 samples\n",
      "Epoch 1/60\n",
      "0s - loss: 1.2551 - k_smape: 172.6832 - val_loss: 1.2857 - val_k_smape: 173.9410\n",
      "Epoch 2/60\n",
      "0s - loss: 1.2399 - k_smape: 165.8086 - val_loss: 1.2709 - val_k_smape: 167.2552\n",
      "Epoch 3/60\n",
      "0s - loss: 1.2244 - k_smape: 160.9729 - val_loss: 1.2565 - val_k_smape: 161.1541\n",
      "Epoch 4/60\n",
      "0s - loss: 1.2117 - k_smape: 155.1527 - val_loss: 1.2370 - val_k_smape: 153.4342\n",
      "Epoch 5/60\n",
      "0s - loss: 1.1923 - k_smape: 149.0116 - val_loss: 1.2196 - val_k_smape: 147.9780\n",
      "Epoch 6/60\n",
      "0s - loss: 1.1767 - k_smape: 145.2636 - val_loss: 1.2014 - val_k_smape: 142.9058\n",
      "Epoch 7/60\n",
      "0s - loss: 1.1593 - k_smape: 140.5019 - val_loss: 1.1818 - val_k_smape: 138.7498\n",
      "Epoch 8/60\n",
      "0s - loss: 1.1397 - k_smape: 136.5430 - val_loss: 1.1668 - val_k_smape: 136.8583\n",
      "Epoch 9/60\n",
      "0s - loss: 1.1224 - k_smape: 134.7621 - val_loss: 1.1469 - val_k_smape: 132.3942\n",
      "Epoch 10/60\n",
      "0s - loss: 1.1052 - k_smape: 130.0300 - val_loss: 1.1328 - val_k_smape: 129.7581\n",
      "Epoch 11/60\n",
      "0s - loss: 1.0885 - k_smape: 127.9231 - val_loss: 1.1235 - val_k_smape: 129.1665\n",
      "Epoch 12/60\n",
      "0s - loss: 1.0746 - k_smape: 127.7193 - val_loss: 1.1005 - val_k_smape: 123.4632\n",
      "Epoch 13/60\n",
      "0s - loss: 1.0522 - k_smape: 121.8633 - val_loss: 1.0940 - val_k_smape: 121.5419\n",
      "Epoch 14/60\n",
      "0s - loss: 1.0346 - k_smape: 119.0359 - val_loss: 1.0804 - val_k_smape: 119.6838\n",
      "Epoch 15/60\n",
      "0s - loss: 1.0259 - k_smape: 118.9465 - val_loss: 1.0757 - val_k_smape: 119.3098\n",
      "Epoch 16/60\n",
      "0s - loss: 1.0101 - k_smape: 115.7908 - val_loss: 1.0609 - val_k_smape: 116.5177\n",
      "Epoch 17/60\n",
      "0s - loss: 0.9755 - k_smape: 113.1004 - val_loss: 1.0518 - val_k_smape: 114.6757\n",
      "Epoch 18/60\n",
      "0s - loss: 0.9736 - k_smape: 111.1015 - val_loss: 1.0514 - val_k_smape: 114.6234\n",
      "Epoch 19/60\n",
      "0s - loss: 0.9491 - k_smape: 109.1864 - val_loss: 1.0521 - val_k_smape: 114.4363\n",
      "Epoch 20/60\n",
      "0s - loss: 0.9537 - k_smape: 110.9464 - val_loss: 1.0490 - val_k_smape: 115.0300\n",
      "Epoch 21/60\n",
      "0s - loss: 0.9464 - k_smape: 107.6489 - val_loss: 1.0383 - val_k_smape: 111.7302\n",
      "Epoch 22/60\n",
      "0s - loss: 0.9386 - k_smape: 108.1339 - val_loss: 1.0394 - val_k_smape: 112.3658\n",
      "Epoch 23/60\n",
      "0s - loss: 0.9044 - k_smape: 104.5730 - val_loss: 1.0333 - val_k_smape: 112.6623\n",
      "Epoch 24/60\n",
      "0s - loss: 0.9182 - k_smape: 106.7350 - val_loss: 1.0224 - val_k_smape: 110.5708\n",
      "Epoch 25/60\n",
      "0s - loss: 0.9033 - k_smape: 105.0194 - val_loss: 1.0278 - val_k_smape: 110.6639\n",
      "Epoch 26/60\n",
      "0s - loss: 0.8897 - k_smape: 103.4790 - val_loss: 1.0251 - val_k_smape: 110.0901\n",
      "Epoch 27/60\n",
      "0s - loss: 0.8968 - k_smape: 104.3175 - val_loss: 1.0191 - val_k_smape: 109.8559\n",
      "Epoch 28/60\n",
      "0s - loss: 0.8734 - k_smape: 101.9441 - val_loss: 1.0264 - val_k_smape: 110.5278\n",
      "Epoch 29/60\n",
      "0s - loss: 0.8814 - k_smape: 101.1664 - val_loss: 1.0245 - val_k_smape: 110.4810\n",
      "Epoch 30/60\n",
      "0s - loss: 0.8774 - k_smape: 103.6383 - val_loss: 1.0203 - val_k_smape: 110.5634\n",
      "Epoch 31/60\n",
      "0s - loss: 0.8704 - k_smape: 101.1655 - val_loss: 1.0388 - val_k_smape: 110.4094\n",
      "Epoch 32/60\n",
      "0s - loss: 0.8550 - k_smape: 99.8032 - val_loss: 1.0142 - val_k_smape: 109.6177\n",
      "Epoch 33/60\n",
      "0s - loss: 0.8528 - k_smape: 99.6322 - val_loss: 1.0126 - val_k_smape: 107.4146\n",
      "Epoch 34/60\n",
      "0s - loss: 0.8383 - k_smape: 98.0331 - val_loss: 1.0150 - val_k_smape: 109.2742\n",
      "Epoch 35/60\n",
      "0s - loss: 0.8349 - k_smape: 98.3235 - val_loss: 0.9954 - val_k_smape: 106.1015\n",
      "Epoch 36/60\n",
      "0s - loss: 0.8372 - k_smape: 97.4628 - val_loss: 1.0144 - val_k_smape: 110.3986\n",
      "Epoch 37/60\n",
      "0s - loss: 0.8339 - k_smape: 99.0715 - val_loss: 1.0028 - val_k_smape: 106.7917\n",
      "Epoch 38/60\n",
      "0s - loss: 0.8326 - k_smape: 97.4468 - val_loss: 1.0170 - val_k_smape: 109.9658\n",
      "Epoch 39/60\n",
      "0s - loss: 0.8241 - k_smape: 96.9354 - val_loss: 1.0023 - val_k_smape: 107.0207\n",
      "Epoch 40/60\n",
      "0s - loss: 0.8207 - k_smape: 96.3039 - val_loss: 1.0037 - val_k_smape: 107.2010\n",
      "Epoch 41/60\n",
      "0s - loss: 0.8113 - k_smape: 96.1783 - val_loss: 1.0137 - val_k_smape: 108.6616\n",
      "Epoch 42/60\n",
      "0s - loss: 0.8048 - k_smape: 95.9038 - val_loss: 1.0014 - val_k_smape: 106.7332\n",
      "Epoch 43/60\n",
      "0s - loss: 0.8137 - k_smape: 94.6655 - val_loss: 1.0092 - val_k_smape: 107.7343\n",
      "Epoch 44/60\n",
      "0s - loss: 0.8136 - k_smape: 96.0251 - val_loss: 1.0075 - val_k_smape: 108.5449\n",
      "Epoch 45/60\n",
      "0s - loss: 0.8039 - k_smape: 95.5082 - val_loss: 1.0074 - val_k_smape: 105.9643\n",
      "Epoch 46/60\n",
      "0s - loss: 0.7919 - k_smape: 93.1709 - val_loss: 1.0064 - val_k_smape: 107.4152\n",
      "Epoch 47/60\n",
      "0s - loss: 0.8149 - k_smape: 96.0155 - val_loss: 1.0092 - val_k_smape: 107.9126\n",
      "Epoch 48/60\n",
      "0s - loss: 0.8074 - k_smape: 96.2923 - val_loss: 1.0111 - val_k_smape: 108.8531\n",
      "Epoch 49/60\n",
      "0s - loss: 0.7933 - k_smape: 94.9591 - val_loss: 0.9940 - val_k_smape: 106.8870\n",
      "Epoch 50/60\n",
      "0s - loss: 0.7880 - k_smape: 93.2140 - val_loss: 0.9924 - val_k_smape: 105.7669\n",
      "Epoch 51/60\n",
      "0s - loss: 0.7880 - k_smape: 94.7189 - val_loss: 0.9922 - val_k_smape: 107.4986\n",
      "Epoch 52/60\n",
      "0s - loss: 0.7823 - k_smape: 92.8808 - val_loss: 0.9975 - val_k_smape: 106.0691\n",
      "Epoch 53/60\n",
      "0s - loss: 0.7822 - k_smape: 92.1634 - val_loss: 0.9937 - val_k_smape: 107.9560\n",
      "Epoch 54/60\n",
      "0s - loss: 0.7945 - k_smape: 94.9049 - val_loss: 0.9863 - val_k_smape: 107.5281\n",
      "Epoch 55/60\n",
      "0s - loss: 0.7699 - k_smape: 91.9543 - val_loss: 0.9895 - val_k_smape: 107.7829\n",
      "Epoch 56/60\n",
      "0s - loss: 0.7774 - k_smape: 93.6807 - val_loss: 0.9917 - val_k_smape: 107.7262\n",
      "Epoch 57/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s - loss: 0.7850 - k_smape: 93.5767 - val_loss: 0.9785 - val_k_smape: 105.1815\n",
      "Epoch 58/60\n",
      "0s - loss: 0.7713 - k_smape: 91.8818 - val_loss: 0.9896 - val_k_smape: 105.6169\n",
      "Epoch 59/60\n",
      "0s - loss: 0.7674 - k_smape: 92.5915 - val_loss: 1.0023 - val_k_smape: 107.1405\n",
      "Epoch 60/60\n",
      "0s - loss: 0.7648 - k_smape: 92.3115 - val_loss: 0.9821 - val_k_smape: 104.5588\n",
      "Wall time: 1h 11min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if load_previous_models:\n",
    "    print('Use Previous Model. Not training.')\n",
    "else:\n",
    "    hists_ens = []\n",
    "    shuffle_indexs_ens = []\n",
    "    for run, models in zip(ens_list, models_ens):\n",
    "        print('=== Run:', run+1, '/', len(ens_list), '===')\n",
    "        hists = []\n",
    "        shuffle_indexs = []\n",
    "        for (gp, model, X_in, Y_in, epochs, A_in) in zip(gp_list, models,\n",
    "                                           X_input_list, Y_input_list, \n",
    "                                           epochs_list, A_input_list):\n",
    "            print('--- Group:', gp+1, '/', len(gp_list), '---')\n",
    "            \n",
    "            # Shuffle data\n",
    "            shuffle_index = make_shuffle_index(len(X_in))\n",
    "            X_in_sh = X_in[shuffle_index]\n",
    "            Y_in_sh = Y_in[shuffle_index]\n",
    "            A_in_sh = A_in[shuffle_index]\n",
    "\n",
    "            hist = model.fit([X_in_sh, A_in_sh], Y_in_sh, batch_size=128, \n",
    "                             epochs=epochs, \n",
    "                             validation_split=0.2, verbose=2)\n",
    "            hists.append(hist)\n",
    "            shuffle_indexs.append(shuffle_index)\n",
    "        hists_ens.append(hists)\n",
    "        shuffle_indexs_ens.append(shuffle_indexs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not load_previous_models:\n",
    "    for run in ens_list:\n",
    "        for gp in gp_list:\n",
    "            models_ens[run][gp].save('../models/' + model_name + '-' + str(run) + '-' + str(gp) +'.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa84a7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa84aa5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa84caac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gp, ep in zip(gp_list, epochs_list):\n",
    "    if ep:\n",
    "        for run in ens_list:\n",
    "            hist = hists_ens[run][gp]\n",
    "            plt.plot(hist.history['loss'], label=str(run))\n",
    "        plt.title('Mean Squared Error (Train) - Group ' + str(gp))\n",
    "        plt.legend(); plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8bb0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8lEX+wPHP7Cab3itpJIEQktCrKF26CgKKqAgqdr3T\n0zv1POvdT1GsIIpnO6woKioqVZHepAYSehJSSEJ6I3V3fn88CyzJpggbEpJ5v177gn2eeeaZZ7P7\nfeaZmWceIaVEURRFaR90LV0ARVEU5dJRQV9RFKUdUUFfURSlHVFBX1EUpR1RQV9RFKUdUUFfURSl\nHVFBX2lxQojbhRCbWrocjRFCxAohdgohhA3yChdCSCGEnfn9CiHErKakvYB9PSWE+PBiymvOp4cQ\nYsvF5qO0LBX0L5AQIkUIUSWE8K21fI/5BxreAmV6SgiRLIQoFUKkCyG+vtRlsDWLgFda63VTCxTn\nP8BrUkophFgphPi3lfJOEkJk/dkALaUcL6X85GILKIQYLoRIr5X3S1LKuy42byllPFAohLjuYvIR\nQkQJIb4SQuQIIYqFEEeFEG8LIUIutowXSwjhIIT4SAhxQghRIoTYK4QY39LlsiUV9C9OMnDzmTdC\niO6Ac0sUxFxLvA0YJaV0BfoBv7VAOS6oNtoEnlJKV4uX1ROaEELflGUNsXYMQogOwAjgB/OiT4AZ\nVmr9twFfSClr/sw+LyNfAPde6MZCiM7AduAk0FtK6Q5cBRwHBtezTXN9p6yxA9KAYYAH8DSwpCUq\ncc1GSqleF/ACUtC+EH9YLHsN+BcggXDzMgfz8lQgG3gPcDKv8wJ+BnKAAvP/QyzyW4dWu9wMlACr\nAd96yrMAeKuB8kYA6835rDGn/9y8bjiQbuX4Rpn/PwDYChQCmeZtDRZpJfAgcBRINi/rat5PPnAY\nmGaR3gdYBhQDO8zHuKmecoeb87erZ/0iYCGwHCgDRtWzzAP41PxZnzD/7XTmPG43f8ZvAnnA/1nZ\nz0zgV4v3TkARMNRimRdQAfQ0v78G2GM+zjTg+fqOy/y3vsv8f735O5MLJJk/W8u0dwAHzX/LJOBe\n83IXoBwwAaXmVxDw/Jm/tTndRCDB/PdcB8TU+rv/HYg3H9/XgKPF+mDzPhwu8HfzOfBTI2mGA+nA\nE0AW8Jl5+d3AMfN3ahkQVN93pNbneebvu8B8TIeAq/9EmeOBqS0dc2z1UjX9i7MNcBdCxJhrk9PR\nvtSWXga6AL2Azmg/mmfN63TA/4COQBjaj2lBre1vQfuR+wMGtB9kfWWZKYT4hxCin5Xa7ZfALsAX\nLchabT+uhxH4m3nbQcDVwAO10lwPDARihRAuaAH/S3O5pwPvCiFizWnfQQuOHYA7za+LcQvwIuAG\nbKpn2dtogT8SrRY3E+1zPWMgWgANMG9XW3e0kxcAUspyYIk5nzOmAYeklPvM78vM6z3RTgD3CyGu\nb8Lx3A1cC/RGu2K7odb6U+b17uZjeFMI0UdKWQaMB07Kc1dEJy03FEJ0ARYDjwB+aCfGn4QQhlrH\nMQ6totADLWieOe4MoBqIbsJxWDMK+K4J6QIBb7Tfxj1CiJHAHHPZOqCduL/6E/sdiHY14Qs8BywV\nQng3tpEQIgDt95vwJ/bVurX0WedyfWGuCaPVGOeg/UjWoF0eSrTah0D74Xey2G4Q5tqwlTx7AQUW\n79cBT1u8fwBY2UCZbgV+Ne8zD3jCvDwMqAFcLNJ+SRNr+lb28wjwvcV7CYy0eH8TsLHWNv9F+7Hp\n0YJGV4t1L9F4Tb+w1ivGvH4R8Gmtbc5bZt5nFRBrsexeYJ35/7cDqY38vT8AXq61bLC5LI7m95uB\nvzWQx1vAm7WOy1pNfy1wn8V2Y2j4aucH4OEG/pbPW/ytnwGWWKzTARnAcIu/+wyL9XOB92rll4HF\nFc6f/N3UAOMs3j9k/gxLgQ8sjqGK868wPgLmWrx3NX+Pwmt/llY+z9vRmpOExfodwG2NlNUe7ff0\n3ws51tb6upRtZW3VZ8AGtFrRp7XW+aG18e+yaPoVaEEIIYQzWpPCOLSmAQA3IYReSmk0v8+yyO80\n2pfdKinlF8AXQgh7tJr3F0KIvWiXtAVSqwmecQIIbcoBmmuHb6DVOp3RTmy7aiVLs/h/R2CgEKLQ\nYpkd2mflx7l2U8uyNMZX1t9OntbIMl+0H7Dlfk6gXXU1lIelArSrhrOklJuEELnA9UKIP9Cawaac\nWS+EGIh2pdcN7SrNAfimkf2A1iRT7+dj7lh8Dq0GqkP7m+xvQr5n8j6bn5TSJIRI4/zPovZ3LqhW\nHm5ogfo8QoghwIozZZZSxlnZfx5aTf3M/hcAC4QQ/wdYduTmSCkrapV7t8V2pUKIPHO5M6zsp7YM\naY7kZ8pH3eOyPBYd2ve1Cu3E1Gao5p2LJKU8gdahOwFYWmt1LlqTTZyU0tP88pBaRyvAY2iXyQOl\n1qE11Lz8ooYESimrpZTfoLVFdkNrh/cyN7ucEWbx/zIsOqDNTUN+FusXorWDRpnL+ZSVMlr+oNKA\n9RbHfKYT9n60NvUazj/hWJblQlibKtZyWS5arbBjrX1m1JPemni0IFvbp2hNODOAVVLKbIt1X6K1\nPYdKKT3Q+nOa8rfNpJ7PRwjhgNY88hoQIKX0RGuiOZNvY8dxEovPwdwRHUrTAidCiGC0E9jh2uuk\nlBvluWYlawEftMEFU+pZd152jZTbBa1vKAPt+wvnD6IIrLV9cK1O9zBznnWY032E1tQ3VUpZ3YTy\nXjZU0LeN2WjNG5Y1aaSUJrRmgTeFEP6g/WiEEGPNSdzQTgqF5vbF5y60AOax7tcIIdyEEDpzbTAO\n2G4+Me0EXhBCGIQQgwHLYXdHAEfz9vZoTVYOFuvd0DojS4UQXYH7GynOz0AXIcRtQgh786u/ECLG\nfAWzFHheCOFsbuf/M/0Lf5p5n0uAF82fT0fgUer2vzRkDdBHCOFYa/mnaM18d6ON6LHkBuRLKSuE\nEAPQ+hmaYgnwVyFEiBDCC3jSYt2ZK4YcoMb8dx5jsT4b8BFCeDSQ9zVCiKvNf+vHgEqgqePvhwFr\npZSVTUxf2/PAECHEG+YTCOZhzzGNbLcYuEMI0ct84nsJ7budIqXMQQv+M4QQeiHEnUCnWtv7o32m\n9kKIG837W17Pvhaa118ntb6bNkUFfRuQUh6XUu6sZ/UTaCMOtgkhitHaCM90gr2FNgokF60jduVF\nFKMYrQaeinbpPRe4X0pp2bE5EG3kw3NYNEVJKYvQ+gs+5FzNyXKs99/N25egncQaHP8vpSxBC0TT\n0WpTWcArnDuRPITWTJWF1v7+vyYcX2GtcfqPNmEbS39BO64ktI7dL4GPm7qxuQa/FphUa3kKWsB0\nQavVW3oA+LcQogSt835JE3f3AbAK2IfWpHH2CtL82f7VnFcB2t9lmcX6Q2gBMkkIUSiEOK8JQ0p5\nGO2q5G207911aMGtqolluxXtiuWCSCmPoH0PQ4B95s9mM9r35JkGtvvVvP47tCuhTmjfrzPuBv6B\n1nwUR92T2HYgCu2YXwRukFLm1d6PuUJwL1r/WpbF9+3WP3+0rZM4v5lLaS+EEM8DnaWUM1q6LJcL\n81XJJ8AA2Q5/OEKIHmidmoNauix/hhDidrROXav3AbQ3qiNXUZpISpkI9G/pcrQUqd2Re1kFfKUu\n1byjKIrSjqjmHUVRlHZE1fQVRVHakVbXpu/r6yvDw8NbuhiKoiiXlV27duVKKf0aS9fqgn54eDg7\nd9Y3+lFRFEWxRgjRlDvbVfOOoihKe6KCvqIoSjuigr6iKEo70ura9BVFUVpKdXU16enpVFRUNJ64\nhTg6OhISEoK9vf0Fba+CvqIoill6ejpubm6Eh4dT90mYLU9KSV5eHunp6URERFxQHqp5R1EUxayi\nogIfH59WGfABhBD4+Phc1JWICvqKoigWWmvAP+Niy9d2gv7pfFg/FzLjW7okiqIorVbbCfpCB+tf\ngYTvW7okiqIoF2zlypVER0fTuXNnXn75ZZvn32aCvknnRBm9qN61ovHEiqIorZDRaOTBBx9kxYoV\nJCYmsnjxYhITE226j7YT9EtKSP3yJCX7UqGwsWdcK4qitD47duygc+fOREZGYjAYmD59Oj/++KNN\n99FmhmzqfXzQuThTVVIKR1dB/7taukiKolzGXvgpgcSTxTbNMzbIneeuq++Z8ZCRkUFoaOjZ9yEh\nIWzfvt2mZWi0pi+E+FgIcUoIcaCe9UIIMV8IcUwIES+E6FNrvbsQIl0IscBWha6nHBgiIqkqd4Mj\nq5pzV4qiKJetptT0FwELsHiQdi3j0R44HIX2wOOF5n/P+A+w4cKL2HSG8HDKNx+H5A1QdRoMzpdi\nt4qitEEN1cibS3BwMGlp55qn09PTCQ4Otuk+Gq3pSyk3APkNJJkEfCo12wBPIUQHACFEXyAAWG2L\nwjbGEB5OdWEFpooKLfAriqJcRvr378/Ro0dJTk6mqqqKr776iokTJ9p0H7boyA0GLHtO04FgIYQO\neB34uw320SSG8HCQkqpKd61dX1EU5TJiZ2fHggULGDt2LDExMUybNo24ONtecTRnR+4DwHIpZXpj\nd5AJIe4B7gEICwu74B0azE/cqnLqjuORVSAltPK76xRFUSxNmDCBCRMmNFv+tqjpZwChFu9DzMsG\nAQ8JIVKA14CZQgirdxpIKd+XUvaTUvbz82v0aV/1Ohv0dR2hOAOyrfY9K4qitFu2CPrL0AK6EEJc\nARRJKTOllLdKKcOklOFoTTyfSimftMH+6qV3dcHOz4+qMidtgRrFoyiKcp6mDNlcDGwFos1DL2cL\nIe4TQtxnTrIcSAKOAR+gNeu0iMryGvThnajKyIag3iroK4qi1NJom76U8uZG1kvgwUbSLEIb+tls\ninLKWfzCdnoG9MdnyxfQ5TpY9zKU5YGLT3PuWlEU5bLRZqZhcPd1xNXbgXTREWN+PsbAqwAJx9a0\ndNEURVFajTYT9IUQdOkfwKkSJyoNHlRVuIFrgGriURRFsdBmgj5AlwGBAGT796MqNRWiRsOx38BY\n3cIlUxRFaZo777wTf39/unXr1iz5t6mg7xngjH+YK1kB/alKSYEu46CyCFK3tXTRFEVRmuT2229n\n5cqVzZZ/mwr6AF0GdqDULZS8pFyIHA46e3V3rqIol42hQ4fi7e3dbPm3mamVz+jcz59NSw6TkutC\nVwc3CB+steuP+b+WLpqiKJeTFU9C1n7b5hnYHcbb/mlYf0abq+m7eDjg71TMSbsITCYTdBkLuUcg\nP6mli6YoitLi2lxNHyAiTLC9wpeTu08Q0mUsrHwSjqyGK+5rfGNFURRo8Rp5c2lzNX2ATn380Rmr\nOLwpDbwjwbeLatdXFEWhjQZ91+hIfPMOkHy8CqPRBFFjIGUTVJa2dNEURVEadPPNNzNo0CAOHz5M\nSEgIH330kU3zb5PNO3YBAQQW7OOUfx/SDxXQscs42LoAktZBzLUtXTxFUZR6LV68uFnzb5M1faHT\nEeh+GnuqOLIjC8KuAAcPONJ8Y18VRVEuB20m6EspOZhZTF5pJQBO4aEElBwkaW8u1TU66DwSjq4G\nk6mFS6ooitJy2kzQTy8oZ/y8jSzbdxLQHqjil7KBmkojKfG5EDUWSrMhK76FS6ooitJy2kzQD/V2\nppOfC2sPnQK0oO+ZdxgXN73WxNNphJYweX0LllJRFKVltZmgDzCyqz/bk/Ipq6zBEN4RgSQ82Ehq\nQj7lwht8oyF5Q0sXU1EUpcW0maBfVFlEkeNP1Oiz2Hws9+zzckPsMzGZJMd350DEUDixVc26qShK\nu9Vmgr5RGlmb+S3O/hv5/fAp7Ly80Ht44HzqKF4dXLQmnoihUF0GGbtburiKoihWpaWlMWLECGJj\nY4mLi2PevHk2zb/NBH1vR2+mRE1G57ab344cRUqJITyc6hMn6DIggMxjRRS7DwCEauJRFKXVsrOz\n4/XXXycxMZFt27bxzjvvkJiYaLP820zQB5gVNwuQFBl+5WBmCYbwcKqSk+nSPwCAowcqILCb6sxV\nFKXV6tChA3369AHAzc2NmJgYMjIybJZ/m7ojN9g1mJGhY/jVuJYViceZFRFO0Y8/4upkIjDSgyM7\nsuk7cBjs+ACqy8HeqaWLrChKK/XKjlc4lH/Ipnl29e7KEwOeaHL6lJQU9uzZw8CBA21WhjZV0we4\nv9ddCH0VPyV/e7Yztyo1lS4DAsg/WUae21AwVkLajpYtqKIoSgNKS0uZOnUqb731Fu7u7jbLt03V\n9AGivaMJcehDWs1vFPtNAaAqJYWwfsMByCzvjI/QQ8pGiBzWgiVVFKU1+zM1clurrq5m6tSp3Hrr\nrUyZMsWmebe5mj7ArNg7EHZlvJe1BdCCvruvI05u9mSnVUJwH9WZqyhKqySlZPbs2cTExPDoo4/a\nPP82GfRvjBuKqOzIqtyl2AUGUpWSghCCgAgPspKLIXwIZOyCypKWLqqiKMp5Nm/ezGeffcbatWvp\n1asXvXr1Yvny5TbLv8017wDo9Tp6uk1hb9WbFAdEYJeSAkBgpDsp8blUBA7B0fQGpG6DqNEtW1hF\nURQLgwcPRkrZbPm3yZo+wLTYsRgr/dnjkENVcgpSSgIjPADIrokGvUEN3VQUpd1ps0F/WLQ/NflD\nOepWjKm4GGNhIX4d3RACslIrIWSAatdXFKXdabNB393Rnl5eI8nycgWgKjkFg6MdPiGuZCcXaVMy\nZMZDeUELl1RRFOXSabNBH+DqrkEk0x+A4/s3AhAQ4UF2cjEyfAggIWVzC5ZQURTl0mrTQX9kV3/S\na4ZRo4P9u7VHJQZGulNVYSRfHwP2zqqJR1GUdqVNB/3O/q4EefmQ5+lKZUoKSUVJ5zpzT5Rrz85V\nQV9RlHakTQd9IQQju/qT6hhMUAEsOrAID38nHFzsyDrTrp9zEEpPtXRRFUVRAKioqGDAgAH07NmT\nuLg4nnvuOZvm36aDPsCIrv6kOgcSVCD4+fgycstzCYzwICupWAv6oGr7iqK0Gg4ODqxdu5Z9+/ax\nd+9eVq5cybZt22yWf5sP+oMifcj28Meu2oRHYQ27sncRGOlOQWYZle5x4OChzcOjKIrSCgghcHXV\nRh1WV1dTXV2NEMJm+bfJO3ItOdrr8YqOgl0QWqAnMS+RGyMGAJCdVkZY+FWQvIHDWSWkF5zm6piA\nFi6xoiitQdZLL1F50LZTKzvEdCXwqacaTWc0Gunbty/Hjh3jwQcfVFMr/1mxA+IAiDvtS2JeIgHh\n7iAgO7mYHN8BkJ/EHW8tZfYnO8kpqWzh0iqK0t7p9Xr27t1Leno6O3bs4MCBAzbLu83X9AEGD+xK\ntp0DAacM/JiXiL2jHld/J37fnMbvOLLCAPd1zODZE74cyChiRFf/li6yoigtrCk18ubm6enJiBEj\nWLlyJd26dbNJno3W9IUQHwshTgkhrJ5qhGa+EOKYECJeCNHHvLyXEGKrECLBvPwmm5T4AoR4OZPn\nGYB7VjUl1SU8sGQ1WwtL0BdUM/SqIZicfLjZLwUhID69qKWKqSiKQk5ODoWFhQCUl5ezZs0aunbt\narP8m9K8swgY18D68UCU+XUPsNC8/DQwU0oZZ97+LSGE54UX9eKI0DA8T5UCsC55NxHR3jhKwX19\nI9BFDME+dROdfF3Yn1HYUkVUFEUhMzOTESNG0KNHD/r378/o0aO59tprbZZ/o807UsoNQojwBpJM\nAj6V2lyg24QQnkKIDlLKIxZ5nBRCnAL8gBaJqsHdo5F7N2OoMTBtqJ67w2NYHL+drKRivCKGQuIP\njOxUyg+pNS1RPEVRFAB69OjBnj17mi1/W3TkBgNpFu/TzcvOEkIMAAzAcWsZCCHuEULsFELszMnJ\nsUGRrBSyezQ6KblShnOi5Ahegc4YnM7cpKU9NnGY4SCnSirJLq5oljIoiqK0tGYfvSOE6AB8Btwh\npTRZSyOlfF9K2U9K2c/Pz69ZyuHUsycAV6Y7k5ifCAICItzJTioGn07gFkRMxV5AtesritJ22SLo\nZwChFu9DzMsQQrgDvwD/klLa7payC2AIC8MhNoYue3IpqSohvSSdwAh38k+WUlVphIgheGVvw14Y\n2Z+u2vUVRWmbbBH0lwEzzaN4rgCKpJSZQggD8D1ae/+3NtjPRXMfNx7nw2n4FEkS8hMIiPRASjiV\nUgxxkxGnc/mb50biM1RNX1GUtqkpQzYXA1uBaCFEuhBithDiPiHEfeYky4Ek4BjwAfCAefk0YChw\nuxBir/nVy/aH0HTu48YCcNURce4mLdDm4ekyDiKHc3vll6SlpTbrMyoVRVFaSlNG79zcyHoJPGhl\n+efA5xdeNNszhIXhGBvL8CMpfJ2XiKOLPV6BztqTtISA8XNxfPdK7qr+nJNF1xDs6dTSRVYURbGp\ndjENgyW3ceMIST1NVtIBpJQERHqQlVys1ez9osmNncVN+nWciFeTsCmK0jKMRiO9e/e26fj8M9pd\n0D/TxNNt/7nO3IrSaopyys3rnyEPd8J3PA8mq4ONFEVRmtW8efOIiYlplrzbXdA3hIUhoyMZdNBE\nQn4CgZHmJ2klFwPg6ObF5653EFSaAPsWt2RRFUVph9LT0/nll1+46667miX/djHhWm0+469FvDWf\n3w9tZ8yYsdg76slKKiJ6YCAAOZGT2bf/F3r8+hwi5lpw9GjhEiuKcqltXHKE3LRSm+bpG+rKkGld\nGkzzyCOPMHfuXEpKSmy67zPaXU0fwGvCNQCIddvQ6QQB4e5na/oA3UK8ebpyFpTlwrpXWqqYiqK0\nMz///DP+/v707du32fbRLmv6hrAw8jt6EfpHmtaZG+HO7lWpVFcasXfQ0yPEg6dkJGnhUwnb8V/o\nMxP8rc9yV5JfQWpCHmkHC+jc15/OfdW0zIrSFjRWI28OmzdvZtmyZSxfvpyKigqKi4uZMWMGn39u\nu4GQ7bKmD1A1rC+dMoykHt1FYKQH0iTJSdVq+10C3DDodSz1mg0GF1jxOJjH7RuNJjIOF7Dlu2Ms\n/vd2Pn1qC+u+OMzxPac4sD69JQ9JUZTL3Jw5c0hPTyclJYWvvvqKkSNH2jTgQzut6QP4X3M9fPor\nGT99S+97/w1oN2kFRXlhsNMR08GN7dk65PB/UfDzfE4uWUFaQShph/KprjCi0wuCojzpOqgDHeN8\niF+XztEdWUiTROhs9zxLRVEUW2q3QT+q2xB+DRR4rd2K02MGPPycyEoqwmSS5KWXMrDGnryjJXx8\nJIaK0rfhd3D1KiKqfwAd43wI6eqFwfHcx+cX6krCBiPFeeV4+Dm34JEpitIWDB8+nOHDh9s833Yb\n9A16A8f6BDB6eRbVGRkERnpwbPcpPnpsI1XlNXgAUgc+sZ5EdyggaMc9uI+Yibj6X1bz8wtzAyAn\ntVQFfUVRWq1226YPUDlU6yEvWrWKqAEBeHdwIaqfP6PvjOXKR3rwgXslYqAPMROH4dF7KGLjqxC/\nxGpePkGu6HSCnLTmGWalKIpiC+22pg/QMWYASYG/oPvlJ6LvvJOOcT5n19UYTTjY6YhPL2JSr2CY\nOB9KMuH7+8DOEWInnpeX3l6HV5ALuakq6CvK5UxKiRCtt1/uYieDbNc1/TjfOLZ21WFKOER1RsZ5\n6+z0OuKC3Nl/5oEq9k5w81cQ3Be+vROOrK6Tn1+YGzlpJWqGTkW5TDk6OpKXl9dqf8NSSvLy8nB0\ndLzgPNp1TT/KM4o/Yu24dV0VxatW43PnHeet7xHiyZKdaRhNEr1OgIMr3PoNfDoRltym/T9i6Nn0\nfqFuHNqSSVlhFa5eDpf6cBRFuUghISGkp6fTXI9ttQVHR0dCQkIuePt2HfQNegMeEdFkhSbjuHJl\nnaDfPdiDRVtSSMopJSpA66jFyRNmfA+LroEvp8Nt30PYQEAbwQOQk1aigr6iXIbs7e2JiIho6WI0\nq3bdvAMQ6xPLpi5GKuLjqUo/v4mnR4g2506dZ+a6+MDMH8EtEL64AU5qT673CXEFAbmqM1dRlFZK\nBX2fWNZFVQFQsmrVeesi/VxxNujZb+3xiW4BMGsZOHrCZ5MhOxGDox1eAc7kqM5cRVFaqXYf9ON8\n4jjlJaiKCqW4VtDX6wTdgjyIr+9B6R4hMOtHbTTPp5Mg9xi+oW5q2KaiKK1Wuw/6UV5R2OnsSO4f\nTEV8PBWJieet7x7iQcLJYmqM9TxQxTsSZi4DaYIlM/ELdqI0v5Ly0qpLUHpFUZQ/p90HfYPeQJRn\nFL/1EOhcXcl977/nre8R4kFljYmjpxqYV9uvC0x8G04l4FekXS3kptp2Hm5FURRbaPdBH7R2/T0V\nR/CaMYOS1aupPHr07LruwVpn7v7anbm1dZ0AcVPwPfgygGriURSlVVJBHy3oF1UWUXnDaISz83m1\n/XAfF9wc7IjPqKdd39L4uTg66XAzFJ6dpllRFKU1UUEfrTMX4JAxA6+bp1O8YgWVyckA6HSCbsEe\njdf0AVz9YPwr+ImD5BzNaDy9oijKJaaCPuc6cxPzEvG54w6EwUDe+x+cXd8jxIODmSVU1dTTmWup\n+434BeooKjZQdfJ4M5ZaURTlz1NBn3OduQm5Cdj5+uI57UaKli07e7NW9xAPqowmjmQ3oZ1eCHxH\nTgYg97tXzz5xS1EUpTVQQd8s1ieWxPxETNKEz+zZCJ2OvA+02n6PYE/Ayp259fDrqt3GnZNaAnts\n+6gzRVGUi6GCvtkVHa6gqLKI9WnrsQ8IwGPqFIqWLqU6K4tQbyc8nOzZ35TOXMDFwwFndwO5DgNh\n1b+gOLOZS68oitI0Kuibjeo4imDXYD7c/yFSSnzuulubxvSjjxFC0CPEg21J+RhNTWuu8QtzI0ff\nG4yVsPzvqplHUZRWQQV9MzudHXd2u5P43Hj+yPoDQ0gwHhMnUrhkCTU5OUzvH0ZybhlLdqY1KT+/\nMDfyc2qoGfwUHPoZEn9o5iNQFEVpnAr6FiZ1noSvky8f7v8QAN977kZWV5O3aBETugfSP9yL11cf\npqSiutG8fENdkSZJXthM6NALlv8DTuc39yEoiqI0SAV9Cw56B26LvY2tmVtJyE3AEB6O+4QJFCz+\nCmNhIc9vzPgOAAAgAElEQVRcG0tuaRXv/N74UEy/UPOD0tNPw6QFUF4AvzyqmnkURWlRKujXMq3L\nNNwMbudq+/fdiywvJ//TT+kR4snUPiF8vCmZ1LzTDebj5uOIg7OdNh1DYHcY/k9I+B7iv74Uh6Eo\nimKVCvq1uBpcubnrzfya+itJhUk4dO6M25gxFHz2OcbiYh4fF41eJ5iz4mCD+Qgh8A11O/eg9MF/\ng7BB8MvfoeDEJTgSRVGUulTQt2JGzAyc7Jz46MBHgFbbN5WWkvXv/2C35HNeL95C+BcL2ffAI6Q9\n8CAnZs4ieeoNFH639Lx8/MLcyMsow2g0gU4Pk81z+nx/L5iMl/qwFEVRVNC3xsvRi6lRU1metJyT\npSdxjInBbfRoin/+mVOvvU7Emu+4OmMPpX/spCojA2kyYiovJ+uFF6hKSTmbj1+YK8YaEwWZ5qYg\nr45wzWuQuhU2vdkyB6coSrumgn49ZsXNAgGLEhYBEPzG63Rev57oXTvpmnCAjE+XcevV/2TXM28T\n/vnnhC36H8JgIOv/XkSaO2vPdOae98zcHjdB3BRYNwcydl/qw1IUpZ1TQb8egS6BXBd5HUuPLiW3\nPBdhb499gD86FxeEEEzsGUTvME9eXXWY0soa7P398Xv4Yco2bTr7rF0Pf2fsHPTnPzNXCLj2DXAN\ngKV3Q1VZCx2hoijtUaNBXwjxsRDilBDiQD3rhRBivhDimBAiXgjRx2LdLCHEUfNrli0Lfinc2e1O\nqoxVfHHwizrrhBA8e20sOSWVLFx3DACvW27GITaG7JfmYCwtQ6cT+IW41n2gipMXXL8Q8o7D6qcv\nxaEoiqIATavpLwLGNbB+PBBlft0DLAQQQngDzwEDgQHAc0IIr4sp7KUW7hHO6I6j+erQV5RU1Z1h\ns3eYF5N7B/PBxmTS8k8j9Ho6PPccNTk55L79NoA2gietFFl7+obIYXDlQ7DzYzi88lIcjqIoSuNB\nX0q5AWjoVtJJwKdSsw3wFEJ0AMYCa6SU+VLKAmANDZ88WqW7ut9FaXUpXx+2Pr7+8XHR6AS8svIQ\nAE49e+I5bRr5n39OxaFD+IW5Ul1ppCinvO7GI5+BgO7w44NQeqo5D0NRFAWwTZt+MGA5IU26eVl9\ny+sQQtwjhNgphNiZk5NjgyLZToxPDFcFX8VniZ9RXlM3cHfwcOLeoZ34OT6TnSnaudH/b4+g9/Ag\n6/kX8A1xBTi/Xf8MOwfklPcpOFDFsVGjKFmtavyKojSvVtGRK6V8X0rZT0rZz8/Pr6WLU8fd3e8m\nvyKf749+b3X9vcMiCXR35LllCRhNEr2nJ/7/+Afle/ei37YanZ2w+qB0U1kZJ1/9H1k7XDGWVZL+\n8CMUPTVem4O/tHWd/BRFaRtsEfQzgFCL9yHmZfUtv+z0DehLb//efHzgYyqNlXXWOxvsePraGBJO\nFvPldu1uW4/rJ+HUry+5r7+Gd4BjnZp+xZEjJN84jeLly/F7+K90/vINnCN9OLk0hYLX/wGvRcGH\no2DDa5B1QM3ZoyiKTdgi6C8DZppH8VwBFEkpM4FVwBghhJe5A3eMedll6cFeD5J9OpuvD1lv27+m\neweu6uzDq6sOk1taiRCCwGefxVhWhmvuMXLSSs6O3y/8/gdSpt2EsbiYsI8/xvf++9F3v4bQ737D\nddgwsnZ6kld9nXbX7tr/wHtXwfzesPtTMDY+w2dtm5Yc5df/JZKVVHS2DIqitE9NGbK5GNgKRAsh\n0oUQs4UQ9wkh7jMnWQ4kAceAD4AHAKSU+cB/gD/Mr3+bl12WBnYYyBUdruDD/R9SVl13bL0Qghcm\ndqO82sgrK7ROXccuXfCeNRPD/o1UltVQnFXMyaefJvOf/8SpRw9Cv/6W6ojuZBwpIO1QPhgcCHl7\nPm7jx3Hqu53kVExBPnoIrpsPTp6w7C+woB/s/RKMNU0q96kTxexbm8aRP7L5bu4uvpmzk4NbMqmp\nNk8DUV0BPz4EaTts9lkpitJ6idZW8+vXr5/cuXNnSxfDqgO5B7j5l5t5oOcD3N/rfqtpXl5xiPfW\nH+e7+6+kb0cvTGVl7Jp6DzvCZ+NTmYrp9GlqAsKp1LtQVX7+/Du9R4dx5dTOSKORzGefpei7pXjP\nmoX/k08gAI6sgt9fhKx48O4Ew5+EblO1eX3qsfK/+0k7VMAtzw0keV8O8esyKMgsw9HFntjBQcQ5\n/oz79mfBPw7u2wS6VtHNoyjKnySE2CWl7NdoOhX0/5xH1z3K5ozNrJi6Am9H7zrryyprGPXGeryc\nDfz0l8HodYKCFav54cscTPaOuAV54x7qi7OnA66eDrh4GHDxdODozlMkbjrJqDtiiR4YiDSZyJ7z\nMgWffYbnjTcQ+PzzCL1ea9s/9Is2jUP2AfDtAsOe0KZ2qBWwC7LK+PKF7fQd25Erru8EgJSSjCOF\n7F+XTvLeHJAmwl0PMNhxAe7T50K3KZfkc1QUxbZU0G8mSYVJTF42mVtjbuXx/o9bTfNLfCYPfrmb\nf0+KY+agcKSUlPz6K07du2MfGGh1G6PRxE/z9pKVVMzkv/chINwdKSU58+eTt/A93K+9lqBX5yKE\n0DYwmeDgMlj3MuQchI5XwW3fg53D2TzXfnqQI39kM/PFK3F2N9TZZ8kn95FwwEB81WS8dSeYErkQ\n3YNbGrxyUBSldWpq0FfX8n9SpGckEztN5OtDX5NZmmk1zYTugQzu7Htep6776NH1BnyTSZKUW8bY\ne7rh7GFgxcJ4yoq07fwffhjfhx6i+OefKduw4dxGOh3EXQ/3b4Fr3oATm8+b0qG0oILD27OIvSrI\nasDn+FrckhdzxXVhDJ8RQ3Z5GPtSu8D+by/q81EUpXVTQf8CPNDzASSShfsWWl0vhOD5iXFUVBt5\n2dypW59DWcVMfW8Lo9/cwLrkPCbc34PKCiMr3tt/trPV9957sA8KIufdd+uOvtHpoP9suOJB2PE+\nHPgOgL1r0pASeo0Krb1LqKmC5Y+DdyRc+Rei+gUQ3sOH7aUzKFz9YZM7iRVFufyooH8BOrh24Kbo\nm/jx+I8kFSVZTdPZ35W7hkTy7a70s3fqWqqoNvLKykNcO38TJ/JOE+juyILfj+ET7MKo22PITi5m\n/ReHkVIi7O3xueceKvbFU7Zli/VCjX4BQgbAsr9SkXqYhE0ZdOkfgLuvU920296FvKMwfi7YOSCE\nYPgtXdHb27E2dSJy7+KL+XgURWnFVNC/QHf3uBtHvSML9iyoN81fRnYmyMORZ35MoMZoOrt849Ec\nxry5gYXrjjO5dzC/PTqMR0d3IeFkMeuP5NCptz/9r43g0LYs9v2mzWThMWUydoGB5L5jpbYPoLeH\nG/8HegPxH35GTZWJ3mPD6qYryoD1cyH6GogafXaxi6cDg2+KIbM6jgM/btGuBhRFaXNU0L9A3o7e\nzIybyZoTa0jITbCaxtlgxzPXxnIws5jPt50gt7SSR77aw20f7UCvE3x590BevbEnXi4Gru8dTAcP\nR95ddxyA/hPC6dTbjy3fHSM1MQ+dwYDPXXdRvns3p7fXM6beI4Sqaz8gPqc/Ef4n8QlyrZtmzTMg\njTDupTqrul7ZgdBwyZacayne8OUFfzaKorReKuhfhFmxs/B08GTe7nn1phnXLZAhUb68tvoIo95Y\nzy/7M/nryM6seHgIV3byPZvOYKfj7iGR7EjOZ2dKPkInGDkrBu8gV1Z/mEBh9mk8b7wBOz8/chda\n70sASMzsQqV0o0/NPG0OH0vJG7Q2/8F/A6/wOtsKIRh+15UIoeP3n8qQVVZmBlUU5bKmgv5FcDW4\nclf3u9iauZUdmdZr39qdunEYTZIof1eW/3UIj46JxtG+7rDI6QNC8XK2P1vbNzjaMeH+7gid4Jd3\n4ykqqMHnrtmc3r6d07t21dneWG1i75pUgqM8CIwOhF8e0+bt0VbC8n+AZ0e46uF6j8nd14krRzmR\nXh7Dwa+sTzCnKMrlSwX9izS963QCnAOYt3tevfPaRPq58sfTo1hy7yCiAtzqzcvZYMedV0Ww9tAp\nEk8WA1oQHn9fdypKq1ny4h9kBA9F5+ND7rt1a/uHd2RRVlRFn/HhMPUjcPSAJTOholgb2ZNzCMa9\nDPZWOnctxF0/jGD3NDZv86D0VGHTPwxFUVo9FfQvkoPegft73k98bjzv7XuPHZk7SC9Jp9p0/sRo\nrg52526sasDMQeG4GPQsXH/87LKgzp5Mf3YAQVGebPg2mcTBT1KwYx/le/eeTWMySXavOoFfmBuh\nMd7g6g83fAwFyfDdbPh9DnQeDdHjGy2D0AlG3BqFyaRj/X/XtdgkbVJK9n21hsKUy3JyVkVplexa\nugBtwaTOk/j2yLe8u+/ds8v0Qk+AcwBBrkEEuQYR6hbKjV1uxMfJp8G8PJztmXFFRz7YmMRjo7sQ\n7usCgIuHA9c+1JP4delsXXqM3AHPULXwB/r/txcASXtyKDpVzti7u507uYQP1p7O9dsLoDfA+Fe0\nB7M3gUfPK7ki4gU2JQ/hyOYUogdHXMAnc3EKd29i0zo9+cdWMuLp2Zd8/4rSFqmgbwN2Ojs+m/AZ\nWWVZZJRmcLL0JBmlGWf/vz1zOz8d/4mNGRtZNHYR9nr7BvObPTiC/21J4b8bjjNnSo+zy4VO0HNk\nKCHRXqx4dSM7KoZS/M5Wht49gN2rTuAZ4Exk71oPobnqEe1RjH7R4NPpTx1X95uv4dirO9i4pIbi\nIvAJdsUn2BV3H0eErmknjwtmMpG6/CdgHCcyPZDlRQgnj+bdp6K0A2runUtkdcpqHlv/GDNiZvDE\ngCcaTf+v7/fzzc50Njw+gkAPxzrrqwqLWXXHm6QGDMHF04GywkpG3NaV2KuCbFrugg/vZeW+q8iv\nDjm7zN5Bj3eQi/kk4EJQlNfZx0LazL6vWPZRBmlVvQGYPjkdn7EzbbsPRWlD1Nw7rcyY8DHcGnMr\nnx/8nDUn1jSa/t6hnTBKyYcbrd/xa/B054pRvvTaOx9ZU42btyPRA6zP7XMxvK77KzcHPsHdQXcy\ndfRBhk/vRNcrO6C303F8zyk2fn2UJS/usPo4yAtWVUb16pc4Wd2dTn20K5fU7Ym2y19R2jEV9C+h\nx/o+Rg/fHjy7+VlSi1MbTBvm48x1PTrw5Y5UCsqs3x3rfdtt+NZkMKLqJ6b9qz96+2b4cwbEwYM7\nMHQdRuD+p4jbex1Dex5l8qO9mf3aEG77v0EYnO3Y+v3xxvNqqi0LyMjzxSjtiBscjLdXJam5AXBy\nj+32oSjtlAr6l5C93p7Xhr2GXqfn0XWPUlFT0WD6+4d35nSVkUVbUqyu17u743XbDMpXL0ectJ7G\nJjxDYdonMHMZ2DvDV7fA51MRecdw93Wi3/hw0hLzSTtogwejFWfC5rdIdZmKnUFHUJQnYb1COVkV\nS9X2zxvfXlGUBqmgf4l1cO3AnMFzOFxwmJd3vNxg2uhAN0bFBLBoSwplldZnvvSeOROdszMZjz9B\n9pw55L73Xwq++YaS337j9O49VJ04gbHERk0vkcPgvo0wdg6k/wHvDoI1z9J9kAdu3o5s/f440nSR\nfURr/4M01nCivDsh0V7o7XWE9QzChD0ndx+CylLbHIuitFMq6LeAISFDuLv73Xx39DuWHV/WYNoH\nRnSiqLyaxTusNwfZeXnh/88nMZWUUPjNt+S89RZZzzxL+oMPceKWWzg+dhxH+g8ge07DJ5gm09vD\noAfgL7ugx02weR76hQMY2O0EOaklHN2VfeF5n9wLe7+kKO5vFOfXEBanDW8N6uSJnT2klsWcnTpa\nUZQLo4ZstpAHej3A3py9/Gfrf4jxjiHKK8pquj5hXgyK9OG99UkM6+Jn9Y5erxtvxOvGGwEwVVRg\nzM+nJr8AY34eNfn5lP72G/mffYbn9JtwiLDReHtXf7j+Heh3B6x+hi6H72ePw3y2L6mhU3cv9I4O\njedhSUrtITDO3pxwuQFIp2M3Lejr7XWEdPXhxOGBsHsh9J1lm2NQlHZI1fRbiJ3OjrlD5+JqcOXR\ndY9SVl1Wb9p/XRODlJLrFmziy+2pDd4hq3N0xD4oCKducbgOHYrn9ddrz9d1cLA6dcNFC+kHdyxH\nzFzKlaEbKC6x48Ccf8C+r8FkbHz7Mw79AikbYfg/ST1yGs8A5/OeBRAW50NxlQ+FJzIga7/tj0NR\n2gkV9FuQr5Mvc4fOJbUklRe2vFBvMO8W7MGKh4fQP9ybp77fz/2f76bwdNPnu7fz8cF7xq0U//wz\nlcdtOMrmDCGg00hCH32fkFATO3NHU/ndI1qbf8IP2vN8G1JTpU357BtNdfeZZBwppGPc+Xcuh8Vp\nD6FPrR4Auz6x/TEoSjuhgn4L6x/Yn7/0/gsrUlZw95q7SS5KtprO392RT+4YwFMTuvLrwWwmzNvI\njuSmj5bxvvNOdE5O5L7zjq2KXofQ6Rg0YwAVRhf2hH2sLfxmFrzTH1Y/AymbrT+K8Y8PIT8Jxr5I\nxrESjDUmwrp5n5fEw88ZDz8nUu3HQPzXUHW62Y5DUdoyFfRbgdndZvP0wKdJzE1kyrIpzN89n/Ka\nunPZ63SCe4Z2YukDV2Kw0zH9/a28sebIeU/lqo+dlxdet91G8YqVVBw+0hyHAYB/R3ei+gewb58T\nZbesg8nvg0cIbFsIiybAq53gu7u0B7CXF8DpfFj/MnQaCZ1HkZqQf3aoZm1hcT5kFAVTU1EOCWra\nZ0W5ECrotwJCCG7qehPLJi9jfPh4Ptj/AZN/nMyG9A1W0/cI8eTnvw5hcu8Q5v92lOnvbyO9oPGa\nr88dt6Nzdm7W2j7AwImRmEySHctToedNMPNHeDwJpn0KXa+B479rM3/O7QT/HQqVJTDm/0AITiTk\nERzthZ2V5w2ExXlTUw2ZTqNht2riUZQLoYJ+K+Lr5MtLQ17i47Ef46B34MHfHuThtQ+TWZpZJ62r\ngx2vT+vJvOm9OJRVwri3NrJoczLGBsbJ6z098Z41i5LVq6k4eLDZjsPDz4luQ4M5uPkk+ZnmDmpH\nd4idBNe/C38/ArN/1Z7g5ewNgx+FgDgKs09TnFNepz3/jOAuXujsBKnOUyBtO5xqvmNQlLZKBf1W\nqH9gf7697lse6fMIWzO3MunHSXyS8InVjt5JvYJZ8fAQ+nT04vmfEpn87mYOZBTVm7f37bPQubmR\ns6B5a/v9JoRj56Bn2w9WOo51egjtD1c/A/du0P4FTiTkAZwdn1+bvYOeoM6epOYHa1NFt+EO3bTE\nfPasaXiqDkW5ECrot1L2entmd5/ND5N+YGCHgby28zXe3vO21bSh3s58ckd/5t/cm5OFFUxcsIn/\n+znR6l28end3vO+4ndLffqP8gPUHutuCk5uBPmM6krwvl8xjTXv6VmpCHp4BWodtfcLifMjPqqAk\n4ibYtxiq295zfItzy1n5/n62LD3G6eKmj9JSlKZQQb+VC3INYv6I+UyNmsoH+z/gkwTrtVshBBN7\nBvHbY8OYPiCMDzclM/qN9fyaWPcOWe+ZM9F5eJD7tvWTiK30vDoUZw8Dm745irGm4c7mmiojGUcK\nzw7NrM+Z9WmuN0BFISQ2fEfz5cZoNLH6owSMRgkSUvbntnSRlDZGBf3LgBCCZ654hrHhY3lt52ss\nPbq03rQeTva8NLk7390/CDdHe+76dCf3fbaLw1klZ5uH9K6u+Nx5J6Xr11O+b99FlS3xZDFzVhyk\nykpQt3fQM2RaF06dKGHjkqMN5pNxpBBjtane9vwzvDu44OrlQGq2D3hHtrkO3R3LkshOLmbU7bG4\n+TiSvDenpYuktDFqGobLhF6nZ87gOZRWl/LC1hdwsXdhbPjYetP37ejNz38dzIcbk5n32xFWJmTh\n42JgQIQ3AyO8GTjqOvSLFnHq7bf57ZErGdNxDCFuIfXmZ01xRTX3fLaT9IJy/FwduGtIZJ00nfv6\nk5Maxu5VqfiGuNJtaLDVvE4cyMPOXkdQl7pDNS0JIQiL9ebYrlMYp8xCv/Y5+OEBCOwBgd0goBs4\nNZxHa5WamMfuVanEDgmic19/so4XcWBDBlUVNRgc1U9VsQ31TbqM2OvteXP4m9y35j6e3PgkLvYu\nDA4e3EB6HfcP78SUPsGsP5zDtuQ8tifls+JAFgAzOg7h1k3L+CViG/sGHWDeyDeaXBYpJf/6/gCZ\nRRXEBbnz1q9HmdgrCH+3uk/5GjipE7npZWz86gjeHVysjsFvaKhmbWFxPiRuziTbZypBXf+AI6tg\n7xfnEniEQkA3sg1XkFwUTf+JXdD7RICuCRe2pTmQtA6SfofkDeDiC4MegtjrQd98P5fTxVX8uugg\n3kEuDL5Rm4cpopcv+9amkZaYT6c+/s22b6V9UUH/MuNk58TbV7/N7FWz+dvvf+P9Me/T2793g9sE\nuDsyrX8o0/qHApBecJrtSfn8eOAghUfgxg2CF4PXklZ0ilCPpgWXb3el89O+k/x9TBcmdO/A2Lc2\nMHflYV67sWedtDqdYMzsWL55eScr39/Pjf/sj5v3uZPDmaGaPUeGNmnfIV29EDpB6rEqgqZ/oU3W\nVpoNWQcgez9kHSDpiIk1qdHU4IDzoX/Rw2uTdiUQ2AMCu2sv/xht29StcHytFujPzOvj6AkRQ7Vh\nod/NhrX/0YJ/7xlgX39H84WQJsmvixKpKq9h0sO9sDdoJ74OnTxwdLEnaV+OCvqKzaigfxlyN7jz\n3qj3uH3l7Tz464N8PO5junp3bfL2IV7O5IQeZn/iF+wbG8mw75N4bKmRDd/fxpCwaHRIpNEERiNS\nam31dr5+2HfogH2HQHKdPHl/TQZDOodx//DO6HWC2YMjeW/9cW4ZGEafMK86+3RwtmfC/T349pWd\nrHhvP5P/3udscDszVLNjt4Y7cS3zCox0JzUhnysmddLm/nEL1F5Ro9j3Wxqb0o4SEO6KzljOHzmz\niY4NxyFvN+z5HM5MbqezA6EHYyXo7CF0IIx8BjqNgA69tKGlJhMc/gU2vQXL/w7rXoaB90L/u7R7\nDGxgz6+ppCXmM+yWaHyCzz1rWKfXEd7Dh+R9uRiNJvR61QWnXDz1YPTLWGZpJjNXzqTKWMUn4z4h\n3CO8ydtN/2U6bgY3Ph/5EUV/eZyU43upkkacdIF08HZB6PUIIUCvB6ORmpwcanJztZqxBZ2HB4bg\nYPSxcbx10kBRRDTv/3MKejvrzTTJ8bksXxhPVL8ARt8ZixCCn97eS1FOOTP+PajJx75zeQrblyVx\nx9zBOLsbADCZJJu/OUr87+lE9vJj1J2xFGSW8c2cnfQZ25FBkztpQTw/CbLitVp9TaX2cJiOV4FD\nAw93lxJObIHNb8HR1WDvok3xHDVG61D2CNFOEn9SdnIxS1/dRURPX8be0037zEGbouLEVpIyfVnx\ndSkTH+5JaEzDndxK+9bUB6OroH+ZSylKYdbKWUgpeWLAE0yImHAucFhxuvo0s1bOIr0knS+u+YJI\nD63z9fuj3/Pslmc5nXIv07oP5aXJ3evkI6uqeOvrLfy2Pp4n+3rRVV9BdeZJqlJSqNh/AFOp9lSr\nGmdXPHr3xKlnT5x69cSpTx/0rucC6pmAfeWUznQfHsyHj20kbkgQQ6Z1afJxnzpRzDdzdjLq9hii\nr+hAdZWRNR8lkLwvl54jQ7nyhs7odFr51/wvgeO7c7j1hSvOa1a6YNkJsHmeNn+QNE8frXcAr3Dw\n6aSdBLwjteajsEHalYgVVeU1fP3iDkwmyU3/GoCji722orocPrgaTiVQLQ18nP0pMW4bGBq5Ebw6\ngmcYeHYEJy9z3uLcPs6819tD51E2b4pSWq+mBn3VvHOZC/cI539j/8fTm5/myY1P8kvSLzxzxTN0\ncO1QJ62Ukmc2P8Ph/MO8c/U7ZwM+wNjwscz9Yy6RXQ+yeEcEXs4GHh93fpPRhpQi5iWUMePaEQy+\nvvv5eZtMVB4/zhvzluKRcoQxp3Ioe+89MJmwCwwk/OuvsQ/Q2qX7ju9IbnopW78/RllhZZOGatbm\nF+qGk5s9JxLyCY314Zd39nEqtYTB06Lq9A1cMakTx3flsP3HJEbdEfun9mNVQBxMeR/GvqS1+ecf\nh7zj2hVEfpLWP3Dm+ceD/wajnq+ThZSSdV8epiS/ksmP9TkX8AFWPAGnEmDSO9g7ehC6tJLknMEM\ncTuOyDuu5V/dhFlGY66Dm9RzhZXzNSnoCyHGAfMAPfChlPLlWus7Ah8DfkA+MENKmW5eNxe4Bu2e\ngDXAw7K1XV5c5iI9I/ls/GcsPrSY+XvmM+nHSTzc52GmR09Hb9Hk8H78+6w+sZrH+j7GkJAh5+Xh\nbO/MhIgJ/Hj8R24YMIN31x3Hy9nA3UO1E0NuaSWPLdlHlwBXnr6mbuAUOh2OUVFM/Od9XLdgE7lX\nhvPMyHDK/viDk48+RvpDD9Hxs0/ROToihODqWTEUZp9m39q0Jg3VrLs/gVukjoN7U8k4nk9ViZHx\n93YnspdfnbRu3o70vDqE3atS6Xl1KH5h/9/eeUdnUXwN+Jkkb3qvhCSQhA6h9yYISAcVAZFiAQQR\nVEDF+tkRFWyIIkoHKSKgINKl14QSSiippPdG2lvn+2MjAikkEAg/2eecPdk6e3dO3rs7d24pWX3s\ntrBzh4CuynI9JhNcTYK9s+Dg1+DeAFo8dcMpZ/cmEB6cQvvBgXjXcbruwG9K7EGXacqkMRDYI4no\nZRdI67oQz9qOiqkpPx20ucXmNlnyb+hqZTRy5TDU7lS55zLqFfNXzVZljlJU/ne55cyQEMIc+B7o\nBzQGnhJC3PyrnwMsl1I2Az4CZhVf2wnoDDQDgoC2QLcqk17lGuZm5oxuPJqNj26klWcrPjv+GU9v\ne5qIrAgAdl/ZzbzT8xgUOIhnmpRebvCJ+k+gNWpp2TCaAU29mfnXBX4NicNkkrz6ayhXi/R891Qr\nrMtxqwzyceKpdrVYfuQKEXkmHLp3p+bsLyg6d46kd969FiCmsTKn/6SmWNtp8G3kWiFXzes5nHiY\n35KlUKYAACAASURBVIqWYaG3oqCwkMemtypV4f9Dq77+WNtpOLQ+vNzKY1WCmRk4+cDArxUPoM0v\nQ+zRa4cjTqRy4NfL+Ddzp1Xf2v9elx4Bm18Bvw7w8LvXdvs3dUcIiA4tjs4VAuw9FFOSe11wrwce\n9cGjAXg2VMxK3d4Eh5qw/e1bF7G5mW1vws89lLoFKv85KuIO0A6IkFJGSSl1wBrg0ZvOaQz8Xby+\n57rjErAGLAErQAPcQeVslVvhY+/D/F7zmdV1FrG5sQz7cxifH/+ctw6+RVP3przf6f0ybf6N3RrT\nyLURGyLW8+XwZnSt586b68/w8ppT7LucxrsDGtGgxq2/kl/r3QB7Kws+2HQeKSUOPXviMXUquVu2\nkPHTz9fOc3S3YcR77ej5TKNKPeOOmB1M3j0Z6Z9DZJ1jnO78O14BjuVeY2VjQduB/iRcyubKuYxK\n3e+2MdfAsGXKJO+aUZB1hYTLWexccp4aAU70Ht/k2rwD+iJY96xyzdBFN8QEWNtrqFnPmajKROda\n2kKv9yHxFJxdV/HrYg4qRW00drDlNcgsvaiPyv8uFVH6PkDcddvxxfuuJxQYUrz+OOAghHCTUh5B\neQkkFS/bpZQl8uEKISYIIUKEECFpaWrY+Z0ihGBg4ED+eOwP+vr3ZeWFlThoHPjm4W+wMi+/YPnQ\n+kO5nHWZ8OwL/Di6tZK7/0wSjzT2YnSH2uVe+w+udpa82rs+hyMz2FYcCOY24XkcBw4k7euvubp7\n97Vz7ZysbrRn34LfLv/G6/tfp6l7U77TjGDMxvU0Xb+PjMiwW17bpKsPTp42HN4QiakChWeqBFtX\nGPkrGPVkLJ7KX/PP4ORuw4DJza65rALKF3nKWXh8gfKSuImA5h5kJuaTnVqJimFNhyuup7s/rFil\nMV0BbHpJmZCesAeEGWyYUHq1M5X/WarK8fc1oJsQ4hSK+SYBMAoh6gKNAF+UF0UPIUTXmy+WUv4k\npWwjpWzj4VH2EF2lcrhauzKr6yxW9FvB0n5L8bS9dYBP/4D+2FjYsD58PXZWFix5ti2v9a7P7KHN\nyvUKupmR7WrRsIYDn2y5QKHOiBAC708+xrppUxJen0HRpUuVfp5FZxfx4ZEP6eTdkS9iO5L52ttY\n2jrQ/YyJlEHDSJgxA2142Tl+zC3M6Ph4HbKS8rlwuGSNgruGez2u9l3K5siRaIy5DJrS9MYX3bkN\nELIIOr0E9UtPrRHQ3B24zsRTEczMlMnm3AQ4WoFU2ntmKhPRg79TTEUDv4L443BgTsXvqXLfUxGl\nnwBc7w7hW7zvGlLKRCnlECllS+Cd4n3ZKF/9R6WUeVLKPGArUHFnbJUqoYVnC/wcKhbtam9pTx//\nPvwV/Rf5+nxc7CyZ0qMezraWlbqnhbkZHwxuQkJ2IT/sVeYVzKyt8Z03D3N7e+InvYghs2I1fqWU\nfBXyFd+c/IYBfn15d68rWV/PxbF/P+pt/pM3X3LiUq+6XN21m6hBg4mbPIXCM2dKbSuwhQfedZw4\ntjkaXdG9+YItytOz+Q9b9BbODHJ4G4eT1/lBZETCppfBty30fL/MNhzdbXDztSc6tJIjYf/OihfP\nga/hajmW1fgQOPoDtH5OmYcAaDoUmj0J+76AuOOVu6/KfUtFlH4wUE8IESCEsARGADfksxVCuAsh\n/mnrLRRPHoBYlBGAhRBCgzIKUMsd3ec8Ue8JCg2FbI3eekftdAh049EWNfnu7wi+2nkZk0mi8fLE\n9/t5GDIyiH/5ZaSu/HzxRpORD458wJLzSxjjO4RJy9PJXb8RtxcmUnPOHCxt7GjYsBPzuuRRd9cu\n3CdPpiAkhJjhT3LluecoCA6+oT0hBJ2G1qUwV3dPipTodUa2/BBKbnoR/ae0wa1THzg8V4kMNmjh\nt+eUoK6hSxR7fjkENncnKTKn8jn2e30IRh3s+aT04wYt/DEZHLzhkY9uPNZ/tjIpvX48FOVW7r7/\nNQxaODwPchOrW5I74pZKX0ppAKYA21EU9q9SyvNCiI+EEIOLT+sOXBJCXAa8gJnF+38DIoGzKHb/\nUCnl5qp9BJWqprlHc+o612X95fV33NYXQ5sxtLVSy3fSLyfI1xqwadoU75kzKQw5QfLHH5fpTZOS\nn8K0vdPYEL6BV2o8xRNfhVBw4gTen36K59SpiOIEal18upBamEqUSMPjpSnU3b0bzxkz0EVEcmXM\n0+QfPXpDuzUCnKjb2pPTO2PJz9be8TOWhcloYsfC8yRH5/LI2Mb41HeBvp9BYHfYPBXWjoakUHhs\nPjjfeiQW0MLj9nLsu9WBdhPg5AolP9HN7J8NaRdh0LdKWcvrsXaCIT9DTpwSP3CnSAkxhyD/Hk2m\nVyXb3oId78Cq4RWbI7lPUSNyVUplZdhKPg/+nN8G/UYD1wZ31JaUksWHYpi5JYz6Xg78/HQb/Fxt\nSf36GzIWLMBxwABsWrfCukEDrOrXJ99KsuTcElaGrcQgDbznMIImX2xCGgz4fvcddu3b3dB+Sn4K\nvX7rxfTW03ku6Llr+00FBUQPeQKTVkvgpj8wd/jX8ygnrZBVHxzFs7YjzjVs0Rca0GmN6IsM6IqM\n6IoM6IuMePo78tCT9XH2sq3UM5tMkn2rLhF2MJGHRtSnaffrJmcLs2BhL8iIgA6Toe+nFe7HFe8c\nwc3HjgGTSya2K5fCLJjbErybw5jf//W/TzoDP3WHZsPh8R/Lvv7vmbD/C2VEEjSk7PPKI/UibHtD\nyWLq0wbGbr+rmUurlNA1sHEi1H0EInZBk8dh6OL7Ko6hohG5agYnlVIZGDgQjZmG3y7/dkftLD+/\nnEm7J2HtdpivR9UmKUcp53g4Mh2PV17GecST5B08SMpHH3Nl1Ggut21HaNeOuL63gNdO1uS33NE0\nen81ZnZ2+K9ZU0LhA3jZeVHPpR6HEg7dsN/M1paan3+GISWFlE9n3XDMycOG1v38yUrOJ/5CJpnJ\nBegKDWiszHH2ssW3vgt1W3uSEp3L6o+PEbwlGqP+1h4/Ukpizqaz9pPjhB1MpHXf2jcqfFDSJ4xe\nr0Tq9vqgwn0phCCghTtxF7IqPx9h46L47kfthfCdyj6jXjHr2LopE77l0W2Goqj/nAo58WWepis0\nEH8pi5y0676Ei3Jg29vwY2fFhbTV05AQAge+rNwzVBfJ55SRWe0u8NQaxRX2/AYl8O5/EPVLX6VM\nZuyfwcH4g+wevhsbi8rncNkUuYl3Dr6Ds5Uz2VqlTm6gY31SkuuQnlqP9x7pxZiO/pikie3Bq9ix\n62fs4zJofdWNhpnWEJsIRiM2LVvi+/08LFzLzmr5VchXrLiwgoMjDmKnsbvhWOq335Ix/0d8532H\nQ69elXqG/Bwth9aFEx6SiksNW7qPakDNeiWziAIkR+dwZEMkieHZOHnY0OGxOtRp5VEpr6dbkXAp\ni9+/PkUHr0i8iyJwHjECmxYtKnYPox5+6KC4Yk46rETs/v2xkqqh0aBbX58RCT92BZ9W8PQfmKQg\nIyGflJhcZYnOJSs5HyTYOGh48q022F3ZALs+gPw0Rdn3fE+JZF7/PJxbD+N2gm/rO+6Xu0ZhtjIS\n0hfCxP3g4KWYqNaPU7yuRq4t0+PqXqMmXFO5Y44nHWfcjnHM7DKTwXUG3/qCm66duGsirT1bM7/X\nfOLz4tkbt5c9cXs4nXoaicSkdybAtg0WtleIzAmntn19+tYch4dFEJn5enJy85FJSfTu1ZLWdcp3\nNz2WdIzxO8Yz9+G5PFzr4RuOSZ2O6BEjMCSnELh5ExZuFcvzs+1cEuZmZjzS2Isr5zPYv/oSuelF\nNOrkTachdTHLSMTC3Z3cPMHRPyKJPJmGjYOGtgMCaNy15l1JhWwymlj08m5cEk4QFLUaqdNh1bgR\nriNH4jhgAGY2t3g5X9wCa0ZC+xcgZDE06A/DK1Fy8tRKIteuINRsImm5zhiKBxzW9hZ4BTjh5e+I\no7sNe1eG4WEVy6N20zH3awX9vlBeFv9QmA3zO4OFFbxwACztSr9fdWIywdpRSlbVZ7dArQ7/HtMV\nwOI+kBUDz/+tREVXM6rSV7ljpJQM3DgQdxt3lvWruGKIzI5kzF9j8LT1ZHn/5Tha3jg5mFGYwd64\nfSw8uZm4wlCkwQFtWh8MuU253uJoaWGGxkygM5r49PGmDGtT9mSnzqijy5ouDK4zmHc7vFvieNHl\ny8Q8MRT77t3wmTv3ll/GoXHZDJl/GKNJMq5LAG/2awhGyfH1Fwndn4yFsYjA8A3kOweQ4NUBc405\nLXvXpsUjte5qacPc7TvYOT+EjBqtefbzTuRv3ULWL6vQhodj5uSE85AhuDw1AstatUpvQEpYNghi\nDoCNK0w+BvYVK9AipeTE1hiObYrGxTwOP6vTeGku46UJx9E8BaGxUYLRrBy5HOvBzpxptGyeR6eJ\nA0uvWhZ9QJGlzXNKyor7jQNfwu6PoO/n0OGFksez45RRgI0zjN9d7WU6VaWvUiUsOruIb05+w2dd\nP7tl2maA9MJ0Rm0ZhdaoZdWAVdS0r1nu+XsuJRKTVoirvTWudpa42FriYmeJi60GG405uYUGJq86\nycGIdF7oVocZfRr8m7rgJl76+yXCs8LZOmRrqXJmLFpE6uw5eH82C+fHHitTpiK9kQFzD5CvNdKr\nsSerDkcz0iyJ53LPoTuwjzxLDy63GEe2pgYCEzWTj+Af9SdOdX1xHj4Mx4EDb0glXVUUnjvPldGj\nyW7ahxN2vRk8tQV+DV2RUlIYEkLmL6u4umsXGI3Yde2C11tvYRUQULKhpDOw4nHFHbOCk7JGg4m9\nv1zk4pFk6rf1pEdfMNdlQkGmMklceN16QSZ4NmJf0hDOHUqj3wulJ8IDYMe7cPg7JWq5ImaSuGAI\nXaVUQKvzsBI9fDeI3AMrh0CTIfDEwrInbGMOwfLBUKeHYu+/jZoKVYWq9FWqhBxtDhN2TiAsI4zu\nft15p/071LCrUeq5BfoCxm4fS1ROFEv6LKGJe5MqkUFvNPHBpvP8ciyW3o29+GZEC2wtS35Nr724\nlk+OfcLmxzaXWlBGGo1cefoZtJcuEbh5ExrvkumnAT7aHMbiQ9H80s+beiF7SV63Hk12Jlet7LEZ\nOJB6Tz+FVb16xIZl4uRhg4ONgZw//yR77a9oL11C2Njg2L8fLsOGYd28eZXY9PUpKcQMGw4W5viu\nXMPyWWE4etjg6m1XnEJfIMxAFmnRX4lBFxGOm20R7Re8jZVNKf7/JmOFFVRRvp6tP54lMTybtgMD\naDvAv0LPZNSb2DDnBNmphQx/uw1OHqV4QBm0SnK3vFR48Yhi7y+9MSVI7MAcpdqZSa/sdw2EwIcV\npRvQVXExvVNy4mHBQ2DnoXzBl1dcByB4EWyZDl2mK5O81YSq9FWqDIPJwMqwlXx/+nvMzcyZ3no6\nQ+sPxUz8O2Q3moxM2zuNvXF7+fbhb0vY1e8UKSVLD8fw8Z9hNKzhyKJn2+DtdKP9Ou5qHP039OfN\ndm8yqtEopJQYTBLNdbZ1XVwcUY8+hk3zZtRatOiar/8/HI5IY86nK3gxMwSfi6fA3Bz7hx4ip3sf\nJkfakJRv5KNHmzCiXUnziZSSorNnyV63jpwtfyELCjB3d8euQwfsOnbErlPHMl805WEqLOTK6DHo\noqOpvXo11g3qc/SPSKJOpRXfV6mzK6VU1qXEcLWQIr0ZFhZQt503jTt5U6OOU6VfQNmpBWz5/gy5\nGYX0GNOIBu1Lf+GXRW56Ib9+GoyDmzVPvN4aC8tSXjQp5xUzSd1HYMQvJb+q08OVHECJJ6H5SOj3\nmRJd/E9d4+gDSglMYY7euwNFtQfg8PBzStK5ymLQwpJ+kHZZyT9UUVv95qlwYonixhn0ROXvWwWo\nSl+lyonLjePDox9yLOkYrTxb8UGnDwhwUswHnx//nJUXVl5TuHeLPZdSeWnVKWwtzfn56TY097vR\njtr3twFY44G/4RUOR2aQpzXw5bDm9Gv6r7LNWvsrye+/j9c77+A6RslZbyosJGX9Rs7P+xmf7GTM\n3NxwfeopnIcPQ+Op2Lyz8nW8vOYUB8LTeapdLT4Y3BirMspCGvPyubpzJ/mHD5N/5AjGdCWgytLf\nH7tOHbHt2BG79u0xdyw/O6g0mUiYOo2rO3fi+8P3ODxcsZepSa/n5NAXSHBsToprc/RaIy41bGnY\nyZuGHbyvlZgsj8TwbP768QwCQb9JTalZ9/Zs1jFn09ny/Rkad6nJw6PLqOV8eJ4S+DT4O8XLB5S3\nWcgi2P4uaKyV4LHGNyf4BQw6iA8mJfg4Ow74kadzYLDvD/g89rRinqnoi64gUwnAOrMGhq+AxpVw\nXjDoFDNP4iklh1KHF6ushnJFUZW+yl1BSsnvEb8zO2Q2WoOWic0nYmVuxZyQOYxuNJo32lVB1OYt\nuJR8lbFLg0nP0zLz8aZYWphxJDKdQxEZpGjWoHE+jlXiJ3QKrElsZgFn4rP5vwGNGdsl4NozxE2c\nSMHxYPzm/0D+oUNkrfsNU04OEc4++D0/lmZjhmJmWVIxGk2SOTsuMX9vJM39nJk/qhU1ncv3mJFS\nor0cTv4R5QVQEByCLCgAc3Ps2rfHoXdvHHr1xMK9pGkj9ZtvyPhxAZ5vvIHbc89Wqp9yt20jYeo0\n3D+ZRZp3Oy4eTiIpMgczM0GtIDdcvW2xstNg/c9i/+96XFgGf6+8iKObDQOnNCvdNFMJjv4eyYlt\nV+j5TCMadixltGMywYpHIf4ETDqopHb+YzJE7IQ6PeHR78Gx9FGSNElO7rjC8U3R2DpbYoGOguwC\nhri8iVsdHyUKumaLsoXLjIKj85XUGPoCeOh16FHSGeCW5KcrZp6wP8DSAdpPhI6T75nyV5W+yl0l\nvTCdWcdmsePKDgB6+PXgq+5f3VCp667eP0/LxBUnOHElCwAHKwvaB7pR0zuGjUkfMr/nfLr4dqFI\nb+Tl1afYEZbC810DeKtfI8zMBPqUVKIGD8aUkwNmZhS278r/WQTRbUhPZvS7dX7/rWeTeG1dKBbm\nZnw2pOkNI4lbIXU6Cs+cIW/fPnJ37EB/JRbMzLBt3RqHPn1weKQXGi8vcjZtInHGGzgPG0qNjz6q\ntGlGmkzEDBuOMSuLwG1bMbO0JCs5nwuHkog4mUp+thaTsezfv08DZ/pOaFqp1NdlYTKa2DT3NClR\nuTzxRhvcfe1LHM+JiiFj+WvkmtelpjiJl9lZRO+Pod3zZX6t52dr2bkkjIRLWdRp5Un3UQ3Qa42s\n/zwE9AU84f42DroIaDUGerynFJ/5h9hjcOQ7uPAnmFkoUckdJyvlMO+ElPPK/EPY72Bpr6TA6DgF\n7O5uYXtV6avcE/bE7uFY8jFeafXKbQVw3QlFeiPbzydT282OoJqOWJibUWQoosuaLgyrP+zaqMNo\nkny4+TzLj1xhYDNvvhzeHCsLc/KPHqMgOBjZbyD914Tj4WDNH5M7Y2lRMf/66PR8pq45RWh8DsPb\n+PL+oCbYWVXOXVMZBVzm6vbt5O7YgS4iEgCb5s0pCgvDpmVLai38GVHKqKMi5B06RNy48Xi9/Tau\nT48pcW+91khRnp6i/OuWPANm5oJGnbwxr2BfVIT8HC2/fhqMxsqczk/UJTMpn8zEfDIS88lKzsdk\nuFEXObtZ0KBLLRq0r1FqQfvoM+n8vewCBr2Rrk/Wp1En72svxoyEPDbMOYmdgzlD2uzA+vQ80Ngq\nkcVOfnBkHsQHg7UztB0H7SYg7b2Iv5CFk6cNju5V8L+cEqakrjj/uxKH0O75YuVfxmT1HaIqfZUH\nlhd2vkBifiKbHvs3GayUkgX7o/hs60XaB7jy05g2ONlqkFLy4i8n2XUhhU1TutDIu3wb+83ojSa+\n2XWZH/ZG4u9mxzdPtigxz1AZtJGRXN2xg9ztygiq1pLFWLiUHgFcHjvOJ1PPywF/N1tinxuL9vJl\n6uzYgbl99QZBJYZn8fvXp5EmRe/Yu1jhWtMet5p2uPrY4VbTHjtdNDFJzlw6nk5ieDYI8G3gQsMO\nNQhs6YkQcHh9BGf3JeDuZ0/vcU1wqVHyuRIuZ7F5bigetewZPNoBzd/vKOYiUFw9O06BFiPB0o6c\ntAL2r7lM7PlMLIpfSk261qyaaOrUC0pSu3MblG23OorLqXdz8G4GNZpXyShAVfoqDywrwlbwRfAX\nbHtiGz72NxZ5++N0Aq+tCyXA3Y4lz7UjODqTqWtP80bfhkzqXue273k0KoNpa0+TdlXL9N71mfhQ\nHczLiCe424Ql5tJ/7gGcbTUse64d9bNiiRn+JO4vTcFj8uQ7bj93xw5y/9yC28QJ2DSpvCkkJSYX\nk8GEa007rGzLNx3lpBVy6WgSl44lk5tehMbKHBsHDbnpRTTv6UfHx+pgril7NBJ5MpVtP5/Dv6k7\n/SYGYRZ7UImmrfcImJljNJg4tSOWkK0xmJkL2vT3Jy4sk/iLWdRq4kqPMY2wcy6/2lyFSbukfPUn\nn1FiJXKuS+3t6KO8BGp3hk5Tbqt5VemrPLBE5UTx6O+P8n8d/o/hDYaXOH44Mp2Jy09ga2VOoc5I\nPS8Hfp3Y8Y6VdE6Bnrc3nmXL2SQ6BLry9ZMtSriV3gumrDrJ3ktpuNhpyMzTsejZtvh+8xH5Bw9S\nZ9fOcnMY3QppMhHZr58yDwE4Dh6E59SpaGqWH4R3p0iTJCkym4tHkslIyKPdoEBqB1Xs6/js3nj2\nr7lM4y416T6qwbWv94TLWexbdYms5ALqtPKky7B62LtYIU2Sc/sTOLw+AnONGd2eakC9tl7lyycl\nabFXyUrKp167GmUGEN5AQSYkn1XSa//zInDygTEbK/RcN6MqfZUHFikl/Tb0o75Lfeb2mFvqOReT\nc3l2cTA5hXq2vtIVf/eqMXtIKVl3Ip4PNp1HY27G3Kda0q3+vSsBGpWWR8+v9jHxoTo828mf0YuO\nEZdZwM/d3fF4+Vlcx4zG6623ym1DZ9RxIfMCzT1Kpm/OO3iIuPHjqfH+e+iTkslctgykxPWZp3Gb\nMOGG9NX3E0f/iOTE1iu0HeBP04d9OfxbBBePJuPgZs1DI+rj37SknT07pYBdS8NIic6lbhtPuo1o\ngLX9vyMTo8FEwqUsokPTiT6Tfq02Q6chdWnZu4w0GLeiEkFzN6MqfZUHmo+PfMyfUX9ycMRBNGVU\npMrK15FdqCegihT+9cSk5/PCyhOEp+bxweAmjKlgUfk75fV1oWwKTeTgGz3wcLAiM1/HM4uPcyEp\nl5VZu3A+uIs627ai8fEp9XopJW8dfIstUVv4oecPdPW9saR13OQpFJ46Rd29ezCztESflETaN9+S\ns2kT5k5OuL/4Ii4jnrztiee7hZSSPSsucuFwEpbW5hh0Jlr0rkWb/v43Fqi/CZPRxMntsQT/GY21\ng4ZuIxpg0BuJDk3nyvkM9EVGLCzN8GvkSkBzD6JOpxEXlsmT77YtdZ7hbqIqfZUHmr9j/+aVPa+w\nuM9i2tZoWyVtFhoKydHmkK3Nvrbk6fLI1+eTp88jT5dHnr54W5eHwWQiP7k3xy7a8Vxnf94d0LjK\n7Pw7r+zEwdKBDt7/Zn6Mzyqg++y9jO5Qmw8G/2trzy3SM35pCNEXolj69+e4DBxIzVml58/fGL6R\n9w6/h4WwoKFrQ1YNWHXNHKJPTCSi1yO4jR+P5/RpN1xXFBZGyhezKTh6FE3tWnh/+CF2HTqUdotq\nw2Q0sWtJGIV5eroMr4dbzYrnR0qLvcqupWFkJuYDSupo/2buBDT3wK+hy7VI4/wcLas/Ooazpy1D\nXm9dMTNPFaEqfZUHmnx9Pl1Wd+HpJk8zrfW0W19wEznaHOaenMvptNNka7PJ0eagNZZdWlEgsNPY\nYaexw15jj72lPQl5CeiNetpbv8v6Y0Z6NvTk26daYl9Jt87r0Rv1fB78OWsvrcXSzJJl/ZYR5B4E\nwPt/nOOXY7Hsm/EwPjcFjBXqjLyw8gT1NixmSOQB6mz6Hat6N6YYiMqOYsSWETR1b0of/z58fPRj\n5vWYRze/bkBxoNiCn6i7a2epIwUpJfn795Py6SwMWVnU2bb1juYPbgdTURGGtDQs/W5dfrKyaDNz\nCP3mNzzaNaF2//ZlKvTLx5PZuTjszsw8t4Gq9FUeeMZuH0uONof1gytX63d37G4+OfoJWUVZdPbp\njKu1K85WzjhZOeFs5XzDuoOlAw6WDthY2NyQiwgg/mo8z2x7BqPJyOPenzJ3Wzb1PO1Z/GzbW0bx\nlkZ6YTqv7n2Vk6knGd1oNHvi9qA36Vk7cC3S4ECXz//m0RY1+WJo6aUUdQYTby7Zz8hvp5HXqDnd\nfl1y7Su+yFDEyL9GklGYwbpB63CxdmHQxkE4WjqyduBa0OsJf7gHNs2a4Tf/h3Ll1EZEEPXY4zg9\nOpiaM2eWe25VYiooIPa5sRSePYv7lMm4T5yIMK+aYEFDZiZx45+nKCwMAMf+/fF8/bVScylJKdn6\n41liz99bM09FlX5xkqb7Z2ndurVUUakKlp5bKoOWBslx28fJA/EHpMlkKvf8jMIM+dre12TQ0iA5\ndNNQGZYedscyRGZHyofWPCR7reslN5w5I4Pe2ybbfLJThsZlVaqdM6lnZI9fe8g2K9rIv6L+klJK\neTHjomy7sq0cvWW0/HhLqAx4808ZlZZXbjt6g1GuePE9GdagoVyz8I9r+z8+8rEMWhokD8QfuLZv\nY/hGGbQ0SO66sktm//mnDGvQUF7dv79C8iZ/8YUMa9BQ5p88WannvF1MWq28MnacDGvUWF55bqwM\na9BQxox5WuqSU+64bV1ysozoP0BeaNZc5mzfLlO/mycvNGsuL7RoKdN++EEai4pKXJOXXSR/nr5P\nrvssWBqN5f/fVRVAiKyAjq12JX/zoip9lapCZ9DJhWcWyh5re8igpUHysd8fkxvDN0qtQXvDeSaT\nSf4V9ZfsurqrbLm8pVwQukDqjLoqk+NixkXZcVVH2W99P3k0Jkp2mrVbNnj3L7nhZJxMyCqQle5F\nOgAAGS5JREFUhTpDuddvuLxBtlzeUvb5rY+8mHHxhmPborfJoKVBssncsXLyLycqJI/+ap482q6L\n3NG6qzx0Ll7uiNkhg5YGyS+Dv7zxPKNe9l/fXw75Y4iMHjlKhvd6RJqMxgrdw3A1T15+qJuMfOxx\naTKU/3zXYzIYZPamzVKfkVGpa+KnTZNhDRrKrN/WS5PJJLPWb5AXWrSUlzp0lLl79lS4rZvRxsXJ\n8J695MVWrWX+8ePX9uvi42Xcy6/IsAYNZXjPXjJnx44SHxWXjiXJeRN3y5Pbr9z2/StDRZW+at5R\n+c+jN+rZGrOVZeeXcTnrMh42HoxsNJJh9YehNWr55Ogn7InbQ1P3pnzU6SPqutStchlC00KZsGMC\n3nbezOmygNd/jeBUbPa147aW5rjYWiqFZOwscbXV0NjHnlTNr6y9tIYO3h2Y/dBsnK1LRvuOXv8B\noXnrGdfwVaa2f7ZC8qTv3U/aCxPZ1KQzG4ecJ9A5gGV9l5XwdNocuZn5G97iy0VGPF9/HbdxYyv8\nzLlbt5IwbTpe//curqNunXlVSknKxx+TtWo1Fh4e1Pzic+w6drzlNckffkj2mrUl5NNGRZEw/VW0\nFy/i+uyzeE6fVimvIm1kJLHPjUVqtfgt/Bmbpk1LnJN/9BgpM2eiDQ/HrlNHvN5+G6u6da/JVlEz\nT1GenlO7YjEZTHQeenulF1WbvorKTUgpOZJ0hGXnl3E48TA2FjZYCAt0Jh0vtXyJ0Y1G39WEccHJ\nwUzaNYlAp0C+77GA01d0pOdpyczXkZWvI7NAR2a+jsz8IlKLYsmxW4uFbQxPNRjDjHbTsTArOQGc\nrzXQ6fNdONZeQZ4I46feP1XYW+nC9BmYtm7m3adtmfPCRuq4lJx0NJqMLH2uE21PXKXR/oNoKjEx\nK6UkduxYis6dVyZ1b1GbOGPRYlJnz8ZpyBAKT59GFx2N2/hxeLz8MkJTuttt2ty5pP8wH7fnx+P5\n6qsljpu0WlK/mE3WL79g3aQJPl99iWXtW7vPFp4/T9z458HCnFqLFmFdv37Zz2kwkLVmLWnffYcp\nPx/fed/h0L07cGtvnqJ8PaG74wj9Ow691kiDdjXo+Wyj20r/oCp9FZVyuJR5ieVhyynQFzC19VRq\nO94bP/oD8Qd4ec/LBLkFseCRBdhqbMkqyuJs+llC00I5k3aGc+nnyNPnYSEsKUgYgq9lZxY+07bU\neIKf90cx868LrJzQjM9DXyRHm8OagWtuWaYSYO7+WbSevpwcC2cOTf+emcNblTjHmJfHxa5d2FdX\nR+AXX9E3oG+lnlcbFUXUo4/hNGgQNT8te1L3nzTQDv364vPll8iiIlJmfUb2unVYN2uGz5zZJer+\nZi5fTsqnsyqUhfTqrl0kvvMu6PW4jhuLdaPGWNWtg8bHp8Rkb8HJk8RNmIiZowO1lyyp0EsCwJCV\nRdz459FGRVF76RJsmisT6peDk9m5KIy6j/iS6WfNU+1qoS00KMp+dxy6QgN1WnnQdkAAbj63X2ZT\nVfoqKvcpO6/s5LV9rxHgGIDepCf2qpLSwFyYU9+lPs08mtHUvSntvdsTnazhxV9OYDRJ5o1sxUPX\nRfcW6Y10/WIP9TztWfV8B6Jzohm5ZSR+Dn4s67es3KynhxIO8cKuF3i5oBNdvt3Pioa9afb2qyUq\ngmWuWkXKRx/z/ZTaxPpasnHwxkqPhlK//JKMnxdSe9UqbFu1LHG84ORJYp99DuugIGotWYyZ1b+5\nbnK3bSfpvffAaKTGB+/jNGgQwLW00w6PPILPN19XyEtHn5hI4ptvUXD8+LV9wsoKy8BArOrUwapu\nHczsHUj98ks0Xl7UWrK40pXODOnpxIwchSk3l9qrVmEVGKDUoJgXStz5DFbZa3mndQApwWloCwwE\nNHen3aAA3H3vPJJZVfoqKvcxW6K2sPDsQmo71qaZRzOauTejsVtjbDUli5XEZRbw/PIQLqdc5e3+\njRjXJQAhBCuOXuH/fj/HqvHt6VRXSSOwP34/U3ZPoa9/Xz7t+inphekk5yeTUpBCSn4KKQUpJOcn\nczTpKF52Xqzqv4r0N/+P7L+2Mq3HNGa/8QQtirOESimJHjwYYWVN+JzneXXfq8zqOouBgQMr9aym\n/HwiBwzE3MWFgHW/Iiz+NVNpo6O5MuIpzF1cqL16VakZRfUJCSS8PoPCkydxevRR7Ls9RMLrM7Bt\n1xa/BQtKLXZTHsarV9FFRqKNjEQbEYk2MgJdRCT6xEQArBo2pNbCn0stalMRdLGxxDw1EjMrK2qv\nXo10c+eZH47Q5kIR1lIZjfg3c6fdwAA8alVd2gpV6auo/IfI1xp49ddQtp1P5olWvnz0aBP6fLMf\nDwcrNkzqdINpY+HZhXx78lsEAsmNv29rc2tq2NXA18GXN9q+gb+TP4asLCIHDCRK2PNJ/1f545Vu\nuNtbURAczJUxT+M9cyaOQx5j2GZl4vv3R38vdX6hPHK3bSdh6lS83n0X8yHDsbe2QGZlEjPiKUz5\n+fivWV3CfHM90mAg/Yf5pP/4I5hMWDdtSq0lS6o0VbQpPx9dfDyW/v43jDZuh8Jz54l9+mk0tWqx\ncsSbLD6dzuzO9UkKTWdVdhYrZ3SlrmfV5ilSlb6Kyn8Mk0ny3d8RfL3rMjUcrUnOLWLh023o1fjG\nDJBSSlZfXE1GUQZetl7UsKtx7a+jpWOptu9/lPKyoAFc6T2UFePakfzaq+QfPkK9vXsws7Fhd+xu\npu6ZyiedP+HRuqXUqi0HKSURz4yl4MwZxvecQT1/Tz7cNx9jRDi1ly29Zv++FQXBweRs2ozH9Gm3\nVWfgXpJ36BCxEyZyxsWfKzM+5Y3BzcjI09L5878Z1Kwms4dV7JkrSkWV/u3Hg6uoqNxTzMwEr/Sq\nR4MaDkz/9TSNvB3p2cizxHlCCEY2Glmpth379iG3d2/G7NnBRK8mfPurkf47d+E6ejRmNsrcQA+/\nHjRybcSPoT/SP7A/GrOKlVFMyS3ihz0R7HfuzrfaYN6J3UnWqRx0iecx+/jzCit8ANu2bbFtWzW5\nlG6HAp0BG415hbxromsHMbf1CKYf/4XOuxcjB36Fm70VT7bxY9XxWKb3rl8tqberrhaaiorKPaFv\nUA32vNadlePaVU1lp2Jq/N+7aGxt+TT8dzLXrAWDgfiu/3rrCCGY3GIy8XnxbI7cfMv2Uq8W8eHm\n83T9Yg+/HIulQ/eW2I15mgZnD9Eh8Ryr2g5h6FkNO8NSquwZqpo8rYFdYSl8sOk8Pb/cS+P3tjNm\nkZK1tDwy8rS8sPIEYY07YT/tVfK2byfl01lIKRnfNRCThEUHou/RU9yI+qWvovI/iJdjyZqxd4qF\nhwde77yNccYbjDKL4Ix3Q97YHE+PcB2v9q5Pk5pOPOT7EEFuQXx/6ns61exEDbsaJdpJyilk8cFo\nVhy9gt4oeaKVDy/1qIefqy2mgrpcORWCXadOvDzuBc4tD2HCihBe79OASd3qlPsS0xtN7LuURkRa\nHqPa18LB+s4Ltt+MwWgiND6bA+HpHAxP53RcNgaTxFpjRvsAN3o28mJtcBwD5h5geBs/pveuj6eD\ndYk2Xl5zirQ8Letf6ISfbw9SsjPJXLIEYW6OS5cuPFPDwKYDF5j8cB1c7KqoMlcFUW36Kioq15BS\nEv/CJPL27cPj22/51SqQH/dGkltkYFDzmkzrVY8is1jGbh+Lh40HS/suRSMcORqZweHIDA5GpBOR\nmoeZgMda+vByj3rlFqgp0ht5/bczbA5N5PGWPswa0hRrzY3ul2GJuaw/Gc8fpxNIz9MB0LCGQ6UT\n16UXpjP/9HwuZl3E0swSS3NLNGaaa39NJnP2XswiKz0AY35jmvo406WuO13qudO6tgtWFopc2QU6\nvvs7guVHYtCYmzGpWx3Gdw3Epji98qy/LrBgfxSzhzZjWBsl26c0mUh6621y/vjjBplMFhZYeXlh\n4eGBhacn1o0b4f7CCxV+putRJ3JVVFRuC0NGBrl/bcVl5FMIc3NyCvX8tD+SxQdj0BlNDGvtS91a\nqcwLewNzoydZkWMxGW2x0ZjTNsCVznXc6NOkRoWrkUkp+X5PBHN2XKaFnzM/Pd0aMyH443Qi60/E\nE5aUi8Zc0LOhF0+09sXCTPDS6lPYWZmz+Nm2NKnpVG77OqOOXy78woIzC9AatbT2bI1BGtCb9OiN\nenRGHTqTjuTcfHSmAoR5Ec3dW/FW+xk0cS+7BnBMej6fbb3ItvPJeDtZM6NvAyzMzHhp9SnGdKjN\nx48FlXhOfUIihtQUDKlpLP8zhPzEZJ4KtEFmpGFIVVJC+/04v0L9djOq0ldRUalS0q5q+X5PBKuO\nxaIzmtDYX8bGdznuloG81/YbOgbUvPY1XB5ZRVnojDpcbVxvmAzedi6JaWtDsdKYkVdkwGCSNPVx\nYmhrXwY1r4mr3b/++BeTc3luSTC5hXrmjWrFww1KTmhLKdkXv4/ZwbOJvRpLN99uvNbmNfyd/Euc\n++WOS3z3dwQzH2+MhdNxfgj9gcyiTAYEDuCVlq/gbV92kNaxqAw+2XKBswk5ALSu7cLq5ztgaVH+\nlGlwTCbDfjzCh4Ob8EynkjJVFlXpq6io3BUSsguJTM2jZS1njqfs59V9r9LGqw3f9/oeK/Oy7dM5\n2hx+OvMTqy6uwmAyIBC4WLvgZuOGh40H7jbuSKM9p6JMBDj78lhQc7r41y81YA0Ur6DnlgRzKeUq\nHz8axMj2//r5R2RF8EXwFxxJOkKgUyAz2s6gs0/nUtvZFZbC+OUhDG/jy+dPNEMIQZ4uj0XnFrEi\nbAVSSsY0HsP4puOxtyw9TYLJJPn9dAI7zqfw4aNNKjznMnT+YZJyitj7enc05nfmV6MqfRUVlXvC\n5sjNvH3wbbr5duPrh78u4cqpN+lZd2kd80Pnk6PN4fF6j9PErQnphemkF6aTVphGRmEGaYVppBem\nYzAZbrje1doVXwdffO198XPwo4ZdDTRmGszNzNEbYOmhK5xPzKNPk5oMbeXH0aQjrL20FluNLZNb\nTGZ4g+FlupfGpOczaN5Barnasn5SpxLzCUl5Scw9NZc/o/7E1dqVSc0nMaz+sCpLzLflbAwvb9jC\nsE4CjW0SjpaOvN729dtqS1X6Kioq94y1F9fyybFP6Ovfl8+6foa5mTlSSvbH72dOyBxicmNoX6M9\nr7d9nQauDcpsR0pJtjabxLxE4vLiiL8a/++SF09SfhImaSpXFjNhxrD6w5jcYjIu1mUHcBXqjDz+\nwyGScor486Uu+LmWPqIAOJ9xnjnBcwhJCeHJBk/ybod3b90pN6E36QlJDuFC5gUuZlzkQuYFruRe\nuRY17WLtQlefrszscnvVxqo0OEsI0Rf4FjAHFkopP7vpeG1gMeABZAKjpZTxxcdqAQsBP0AC/aWU\nMRV/FBUVlfudJxs+SYGhgK9OfIWNhQ2jGo1iTsgcjiYdxd/Rn3k95vGQ70O3jCsQQjH5uFi7lDqJ\nqjfpySjMwGAyYJRGjNKIyWTCKI38GhLLksORNPSoSQ/3Tjhblaw98A9SSt75/SyXUq6y+Nm25Sp8\ngCZuTVjcZzFfnfiKpeeXUte5LiMajqhY56BMJr/090scTjwMgLedNw1dG9I/sD/ZWR78tEvHt6N6\n0qOR1y1aunNu+aUvhDAHLgOPAPFAMPCUlDLsunPWAX9KKZcJIXoAz0kpxxQf2wvMlFLuFELYAyYp\nZUFZ91O/9FVU/neZd2oeC84sAMDJyolJzSeVa16pav46m8QHm86TelVL69ouTOlRl+71PUq8bFYe\nvcK7v59jaq96TO1Vdq78mzGajLyy5xUOJhzkx0d+pIN3h1teYzAZmLF/Bjuv7OTNdm8yIGDADcVw\n9EYT3WfvxcfZhl9fKL9oTHlUmXlHCNER+EBK2ad4+y0AKeWs6845D/SVUsYJpXdzpJSOQojGwE9S\nyi4VFVxV+ioq/7tIKVl0bhG5ulzGBY3Dyap8d8q7QZHeyLqQOObvjSQxp4hmvk5MebgujzT2QgjB\nqdgshi84Que67ix+pm2Jwia3Ik+Xx5itY0gtSGXVgFXl1mKQUvL+4ffZGLGRGW1nMKbxmFLPW3Io\nmg83h7F+Ukda1654oZrrqajSr8h0sQ8Qd912fPG+6wkFhhSvPw44CCHcgPpAthBigxDilBBidvHI\nQUVF5T+IEILxTcczvfX0alH4ANYac8Z09Gfv6w/z2ZCmZBfombDiBP2+PcBvJ+J58ZeTeDla882T\nLSqt8AHsLe2Z22MuZsKMKbunkKsrPSWDlJI5IXPYGLGRic0mlqnwAZ5s64eLrYb5e6MqLU9lqarc\nO68B3YQQp4BuQAJgRJkz6Fp8vC0QCDx788VCiAlCiBAhREhaWloViaSiovIgY2lhxoh2tfj71W58\nOaw5OqOJ19aFkpmv48fRrXG2rVwe/uvxc/Dj6+5fE58Xz4x9M0p4HAH8dOYnloctZ2TDkUxuMbnc\n9mwtLXilZz3a+rtwt51rqsS8c9P59sBFKaWvEKID8LmUslvxsTFAByllmT2gmndUVFTuBkaTZGdY\nMo42GjrVub0CKTezIXwD7x9+n9GNRvNGuzeu7V91YRWzjs9iUOAgPunyCWbi7ue2rErvnWCgnhAi\nAOULfgRwQ95WIYQ7kCmlNAFvoXjy/HOtsxDCQ0qZBvQAVI2uoqJyzzE3E/QNqlz5w1sxpN4QwrPC\nWXlhJXWc6zC0/lA2R25m1vFZdPfrzoedP7wnCr8y3FLpSykNQogpwHYUl83FUsrzQoiPgBAp5Sag\nOzBLCCGB/cDk4muNQojXgN3FE7wngJ/vzqOoqKio3HtebfMq0bnRzDw6k5SCFH4+8zPtarRjTrc5\n98xrqTKowVkqKioqd8hV3VVG/TWK6JxogtyCWNhnIXaaqivlWBHUylkqKioq9wgHSwe+7/k9qy+u\nZkLTCfdc4VcGVemrqKioVAF+Dn7MaDujusW4JffXDIOKioqKyl1FVfoqKioqDxCq0ldRUVF5gFCV\nvoqKisoDhKr0VVRUVB4gVKWvoqKi8gChKn0VFRWVBwhV6auoqKg8QNx3aRiEEGnAlTtowh1IryJx\nqhpVtttDle32UGW7Pf5XZastpfS4VQP3ndK/U4QQIRXJP1EdqLLdHqpst4cq2+3xX5dNNe+oqKio\nPECoSl9FRUXlAeK/qPR/qm4BykGV7fZQZbs9VNluj/+0bP85m76KioqKStn8F7/0VVRUVFTKQFX6\nKioqKg8Q/xmlL4ToK4S4JISIEEK8Wd3yXI8QIkYIcVYIcVoIUe21IIUQi4UQqUKIc9ftcxVC7BRC\nhBf/dblP5PpACJFQ3HenhRD977VcxXL4CSH2CCHChBDnhRCvFO+/H/qtLNmqve+EENZCiONCiNBi\n2T4s3h8ghDhW/HtdK4SwvI9kWyqEiL6u31rca9muk9FcCHFKCPFn8fad95uU8n9+QSnYHgkEApZA\nKNC4uuW6Tr4YwL265bhOnoeAVsC56/Z9AbxZvP4m8Pl9ItcHwGv3QZ95A62K1x2Ay0Dj+6TfypKt\n2vsOEIB98boGOAZ0AH4FRhTv/xGYdB/JthQYWt3/c8VyTQdWAX8Wb99xv/1XvvTbARFSyigppQ5Y\nAzxazTLdt0gp9wOZN+1+FFhWvL4MeOyeCkWZct0XSCmTpJQni9evAhcAH+6PfitLtmpHKuQVb2qK\nFwn0AH4r3l9d/VaWbPcFQghfYACwsHhbUAX99l9R+j5A3HXb8dwn//TFSGCHEOKEEGJCdQtTBl5S\nyqTi9WTAqzqFuYkpQogzxeafe24+uRkhhD/QEuXL8L7qt5tkg/ug74pNFKeBVGAnyqg8W0ppKD6l\n2n6vN8smpfyn32YW99vXQgir6pAN+AaYAZiKt92ogn77ryj9+50uUspWQD9gshDioeoWqDykMna8\nX7545gN1gBZAEvBldQojhLAH1gNTpZS51x+r7n4rRbb7ou+klEYpZQvAF2VU3rA65CiNm2UTQgQB\nb6HI2BZwBd6413IJIQYCqVLKE1Xd9n9F6ScAftdt+xbvuy+QUiYU/00FNqL8499vpAghvAGK/6ZW\nszwASClTin+YJuBnqrHvhBAaFKX6i5RyQ/Hu+6LfSpPtfuq7YnmygT1AR8BZCGFRfKjaf6/Xyda3\n2FwmpZRaYAnV02+dgcFCiBgUc3UP4FuqoN/+K0o/GKhXPLNtCYwANlWzTAAIIeyEEA7/rAO9gXPl\nX1UtbAKeKV5/BvijGmW5xj8KtZjHqaa+K7anLgIuSCm/uu5QtfdbWbLdD30nhPAQQjgXr9sAj6DM\nOewBhhafVl39VppsF697iQsUm/k97zcp5VtSSl8ppT+KPvtbSjmKqui36p6drsJZ7v4oXguRwDvV\nLc91cgWieBOFAufvB9mA1SjDfT2KXXAcir1wNxAO7AJc7xO5VgBngTMoCta7mvqsC4rp5gxwunjp\nf5/0W1myVXvfAc2AU8UynAPeK94fCBwHIoB1gNV9JNvfxf12DlhJsYdPdS1Ad/713rnjflPTMKio\nqKg8QPxXzDsqKioqKhVAVfoqKioqDxCq0ldRUVF5gFCVvoqKisoDhKr0VVRUVB4gVKWvoqKi8gCh\nKn0VFRWVB4j/BxV+DoDpYQwhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa84ffc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5nYNfLltAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa933aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gp, ep in zip(gp_list, epochs_list):\n",
    "    if ep:\n",
    "        for run in ens_list:\n",
    "            hist = hists_ens[run][gp]\n",
    "            plt.plot(hist.history['val_loss'], label=str(run))\n",
    "        plt.title('Mean Squared Error (Validation) - Group ' + str(gp))\n",
    "        plt.legend(); plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuYzzRu+ccxnnjd455zLuf0sMGXtcV05MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa97c72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa97e0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa98e8080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gp, ep in zip(gp_list, epochs_list):\n",
    "    if ep:\n",
    "        for run in ens_list:\n",
    "            hist = hists_ens[run][gp]\n",
    "            plt.plot(hist.history['k_smape'], label=str(run))\n",
    "        plt.title('SMAPE (Train) - Group ' + str(gp))\n",
    "        plt.legend(); plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njjb0Go1GM8D5f8QxT6B396bLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9866710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9806f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa849fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gp, ep in zip(gp_list, epochs_list):\n",
    "    if ep:\n",
    "        for run in ens_list:\n",
    "            hist = hists_ens[run][gp]\n",
    "            plt.plot(hist.history['val_k_smape'], label=str(run))\n",
    "        plt.title('SMAPE (Validation) - Group ' + str(gp))\n",
    "        plt.legend(); plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa97a3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSinlQ/4fuhQeFCAS+jgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9932518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8b989e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for (hist, gp, ep) in zip(hists, gp_list, epochs_list):\n",
    "    if ep:\n",
    "        plt.plot(hist.history['loss'], label='train')\n",
    "        plt.plot(hist.history['val_loss'], label='validation')\n",
    "        plt.title('Mean Squared Error - ' + str(gp))\n",
    "        plt.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8VFXawPHfSe+BFBJIgIQWIBBCR5qgSF3BgoKdtaDY\nd9dXV7fo7ur7uuqqa1kVV8WCKCIoAlZ67y30FgQCJAHSe3LeP84kJCGNSZmS5/v5zCcz987ceeZC\nnpx5zrnnKK01QgghnJeLrQMQQgjRuCTRCyGEk5NEL4QQTk4SvRBCODlJ9EII4eQk0QshhJOTRC+E\nEE5OEr1wKEqpoUqpdUqpdKXUeaXUWqVUf8u+aUoprZR6rdJrJlm2z6q03U8plaWU+r6K90lUSuVa\n9p9VSs1SSvlZ9q1QSuVZ9pXevqvHZ5qplDqglCpRSk2z9jhCVEcSvXAYSqkAYBHwJhAERAB/A/LL\nPe0IcLNSyq3ctruAg1Uc8kbLa69RSoVXsf9arbUf0AfoB/y53L6HtdZ+5W7XWvu5gJ3Ag8C2ehxD\niGpJoheOpAuA1nqO1rpYa52rtf5Ja72r3HPOALuBMQBKqSBgMLCwiuPdBbwL7AJur+5NtdangO+B\nHg3yKS49/tta66VAXmMcXwhJ9MKRHASKlVIfK6XGKaVaVvO8T4A7LfenAt9SsdWPUqo9MAKYbbnd\nSTWUUm2B8cD2ekUvhI1IohcOQ2udAQwFNPA+kKKUWqiUCqv01AXACKVUICaBf1LF4e4Admmt9wJf\nALFKqd6VnvONUioNWAOsBP633L43lFJp5W7/qPcHFKKRSKIXDkVrvU9rPU1rHYkppbQBXq/0nFxg\nMaamHqy1XlvFoe7EtORLSzMrMaWc8q7TWrfQWrfXWj9oOW6pRy37Sm9/qSpepdSech22w6z5zELU\nlyR64bC01vuBWVRdO/8E+APwWeUdSqnBQGfgaaXUGaXUGWAgcGulTtyGiDG2XIft6oY8thB1JYle\nOAylVFel1B+UUpGWx22BW4ANVTx9JXANZoROZXcBPwPdgXjLrQfgDYxrhNBrpJTyUEp5AQpwV0p5\nKaXkd1M0GPnPJBxJJqblvVEplY1J8AmYlnsF2liqtT5ffrslod4MvKm1PlPudgz4lEvLN9V5q9I4\n+q31+Fw/AbmY0UEzLfeH1+N4QlSgZOERIYRwbtKiF0IIJyeJXgghnJwkeiGEcHKS6IUQwsk16Jhh\na4WEhOioqChbhyGEEA5l69atqVrr0NqeZxeJPioqii1bttg6DCGEcChKqeN1eZ6UboQQwslJohdC\nCCcniV4IIZycXdTohRDOpbCwkJMnT5KXJ2upNAQvLy8iIyNxd3e36vWS6IUQDe7kyZP4+/sTFRWF\nUsrW4Tg0rTXnzp3j5MmTREdHW3UMKd0IIRpcXl4ewcHBkuQbgFKK4ODgen07kkQvhGgUkuQbTn3P\npUMn+lNpufzrpwMkpmbbOhQhhLBbDp3o03IKeHPZYfadzrB1KEIIO5KWlsZ//vOfy37d+PHjSUtL\na4SIbMuhE314gBcAZzKkZ18IcVF1ib6oqKjG1y1ZsoQWLVo0Vlg249CjboJ8PfBwdZFEL4So4I9/\n/CNHjhwhPj4ed3d3/Pz8aN26NTt27GDv3r1cd911nDhxgry8PB577DGmT58OXJyOJSsri3HjxjF0\n6FDWrVtHREQE3377Ld7e3jb+ZNZx6ESvlKJVgCdn0yXRC2Gv/vbdHvYmNWx5tXubAJ69Nrba/S++\n+CIJCQns2LGDFStWMGHCBBISEsqGJ3744YcEBQWRm5tL//79ufHGGwkODq5wjEOHDjFnzhzef/99\nbr75Zr7++mtuv/32Bv0cTcWhEz2Y8o206IUQNRkwYECFMehvvPEGCxYsAODEiRMcOnTokkQfHR1N\nfHw8AH379iUxMbHJ4m1oDp/owwK82CudsULYrZpa3k3F19e37P6KFSv45ZdfWL9+PT4+PowYMaLK\nMeqenp5l911dXcnNzW2SWBuDQ3fGgkn0ZzPykEXOhRCl/P39yczMrHJfeno6LVu2xMfHh/3797Nh\nw4Ymjq7pOXyLPjzQk5yCYjLziwjwsm4eCCGEcwkODmbIkCH06NEDb29vwsLCyvaNHTuWd999l7i4\nOGJiYhg0aJANI20aDp/owyxDLM+m50miF0KU+fzzz6vc7unpyffff1/lvtI6fEhICAkJCWXbn3ji\niQaPryk5fOlGxtILIUTNHD/RB1oSvQyxFEKIKjl8oi8r3UiLXgghquTwid7L3ZUWPu5SuhFCiGo4\nfKIHy0VT6fm2DkMIIeySUyT60rH0QgghLlVroldKfaiUSlZKJZTb9pxS6pRSaoflNt6yPUoplVtu\n+7uNGXypsABPKd0IIazm5+cHQFJSEpMnT67yOSNGjGDLli01Huf1118nJyen7LG9THtclxb9LGBs\nFdtf01rHW25Lym0/Um77Aw0SZS3CA7xIzcqnqLikKd5OCOGk2rRpw7x586x+feVEby/THtea6LXW\nq4DzTRCL1cICvdAaUrKkTi+EMNMUv/3222WPn3vuOZ5//nmuvvpq+vTpQ8+ePfn2228veV1iYiI9\nevQAIDc3l6lTpxIXF8eUKVMqzHUzY8YM+vXrR2xsLM8++yxgJkpLSkpi5MiRjBw5EjDTHqempgLw\n6quv0qNHD3r06MHrr79e9n7dunXjvvvuIzY2ltGjRzfKnDr1uTL2YaXUncAW4A9a6wuW7dFKqe1A\nBvBnrfXqql6slJoOTAdo165dPcIod9FUeh6tAx1zvmghnNb3f4Qzuxv2mOE9YdyL1e6eMmUKjz/+\nOA899BAAc+fO5ccff+TRRx8lICCA1NRUBg0axMSJE6tdj/Wdd97Bx8eHXbt2sWvXLvr06VO274UX\nXiAoKIji4mKuvvpqdu3axaOPPsqrr77K8uXLCQkJqXCsrVu38tFHH7Fx40a01gwcOJArr7ySli1b\nNsl0yNZ2xr4DdATigdPAvyzbTwPttNa9gd8DnyulAqo6gNZ6pta6n9a6X2hoqJVhGDKWXghRXu/e\nvUlOTiYpKYmdO3fSsmVLwsPDeeaZZ4iLi2PUqFGcOnWKs2fPVnuMVatWlSXcuLg44uLiyvbNnTuX\nPn360Lt3b/bs2cPevXtrjGfNmjVcf/31+Pr64ufnxw033MDq1aYN3BTTIVvVotdal50dpdT7wCLL\n9nwg33J/q1LqCNAF0+pvNHJ1rBB2rIaWd2O66aabmDdvHmfOnGHKlCnMnj2blJQUtm7diru7O1FR\nUVVOT1ybY8eO8corr7B582ZatmzJtGnTrDpOqaaYDtmqFr1SqnW5h9cDCZbtoUopV8v9DkBn4Gh9\ng6xNkI8H7q6KMxlSoxdCGFOmTOGLL75g3rx53HTTTaSnp9OqVSvc3d1Zvnw5x48fr/H1w4cPL5sY\nLSEhgV27dgGQkZGBr68vgYGBnD17tsIEadVNjzxs2DC++eYbcnJyyM7OZsGCBQwbNqwBP23Nam3R\nK6XmACOAEKXUSeBZYIRSKh7QQCJwv+Xpw4G/K6UKgRLgAa11o3fkurgoWvnLWHohxEWxsbFkZmYS\nERFB69atue2227j22mvp168f8fHxdO3atcbXz5gxg9/+9rfExcURHx/PgAEDAOjVqxe9e/cmNjaW\nDh06MGTIkLLXTJ8+nbFjx9KmTRuWL19etr1Pnz5Mmzat7Bj33nsvvXv3brJVq5Q9LNjRr18/Xdv4\n1Nrc+M46PFxdmDPd+eeWFsLe7du3j27dutk6DKdS1TlVSm3VWver7bVOcWUsmJE30qIXQohLOU2i\nb2W5OtYevqEIIYQ9cZpEHx7gRU5BMVn5RbYORQgB0uhqQPU9l86T6ANlLL0Q9sLLy4tz585Jsm8A\nWmvOnTuHl5eX1cdw+DVjS4WVXR2bT6dW/jaORojmLTIykpMnT5KSkmLrUJyCl5cXkZGRVr/eaRK9\nrB0rhP1wd3cnOjra1mEICyndCCGEk3OaRO/l7kqgt7tMgyCEEJU4TaIHy5KC0qIXQogKnCrRhwXK\nRVNCCFGZcyV6f08p3QghRCVOlejDA2VJQSGEqMypEn1YgBclGlKzCmwdihBC2A2nSvQyll4IIS7l\nXIleVpoSQohLOFWil7VjhRDiUk6V6IN9S5cUlEQvhBClnCrRly0pKKUbIYQo41SJHiDMsgCJEEII\nw+kSfXigTIMghBDlOV2il9KNEEJU5HSJPjzQi+yCYjLzCm0dihBC2AXnS/RlQyzzbRyJEELYB6dL\n9DKWXgghKnK6RC9XxwohREXOl+hlvhshhKjA6RK9t4crAV5uUroRQggLx070F47DT3+GtBMVNocH\neknpRgghLBw70Rfmwro34cjSCpvDAmRJQSGEKOXYiT40Bvxbw9EVFTbLIuFCCHGRYyd6paDDSDi6\nEkouLh8YFuBFSqYsKSiEEODoiR6gwwjIPQ9ndpVtCguUJQWFEKKUcyR6gKPLyzaFy0VTQghRxvET\nvX8YtOoORy5N9FKnF0IIZ0j0YOr0v24wo3CAsEBPQFr0QggBTpPoR0BxPvy6HoAQX0/cXJSMpRdC\nCJwl0UcNARf3smGWZklBWWlKCCGgDoleKfWhUipZKZVQbttzSqlTSqkdltv4cvueVkodVkodUEqN\naazAK/DwhbYDK9TpwwLloikhhIC6tehnAWOr2P6a1jreclsCoJTqDkwFYi2v+Y9SyrWhgq1RhxFm\niGX2OcBy0ZSUboQQovZEr7VeBZyv4/EmAV9orfO11seAw8CAesRXdx1Hmp/HVgCl0yDI4iNCCFGf\nGv3DSqldltJOS8u2CKD8DGMnLdsaX+t48AwsK9+EBXiRlV9EVn5Rk7y9EELYK2sT/TtARyAeOA38\n63IPoJSarpTaopTakpKSYmUY5bi6QfQw0yGrNeGWIZZSvhFCNHdWJXqt9VmtdbHWugR4n4vlmVNA\n23JPjbRsq+oYM7XW/bTW/UJDQ60J41IdR0L6CTh/tGxJwWTpkBVCNHNWJXqlVOtyD68HSkfkLASm\nKqU8lVLRQGdgU/1CvAwdLHX6o8vl6lghhLBwq+0JSqk5wAggRCl1EngWGKGUigc0kAjcD6C13qOU\nmgvsBYqAh7TWxY0TehWCOkBgOziynPBe0wBJ9EIIUWui11rfUsXmD2p4/gvAC/UJympKQYcrYe9C\nfNwU/l5unJUavRCimXOOK2PL6zgS8tMhabssQCKEEDhjoo++0vw8utysHStj6YUQzZzzJXrfEAiP\ngyMrzEVTUroRQjRzzpfowZRvTmykrW8JKVn5FJdoW0ckhBA245yJvsMIKCmkV8leiks0B89m2joi\nIYSwGedM9O2uAFdPBuldeLm78PG6RFtHJIQQNuOcid7dG9oNwuvEam7sE8n87adIzZJOWSFE8+Sc\niR5MnT55D/f19qWgqITPNhy3dURCCGETzpvoO4wAICpjC1d3bcWn64+TV9h0F+kKIYS9cN5EH94L\nvIPgyHLuGRbNuewCvtle5fxqQgjh1Jw30bu4mOkQji7niuggYtsE8N81x9BahloKIZoX5030YGaz\nzDyNStnHvcOiOZycxcqDDTD3vRBCOBDnTvRdLGuT71/ChJ5tCAvw5L+rj9k2JiGEaGLOnej9wyGi\nHxxYgoebC3cNjmLN4VT2nc6wdWRCCNFknDvRA3QdD0nbIOM0tw5oh7e7Kx+skVa9EKL5cP5EHzPe\n/DywhBY+HtzUL5Jvd5ySJQaFEM2G8yf60K7QMhoOfA/A3UOiKSrRfLJeLqASQjQPzp/olTKt+mMr\nIT+TqBBfrukWxmcbj5NbIBdQCSGcn/MnejB1+uICOLwUgHuHdSAtp5Cvt520cWBCCNH4mkeibzsI\nvFuWlW/6R7UkLjKQD9cco0TmqhdCOLnmkehd3aDzGDj0IxQXoZTinqHRHE3NZtn+ZFtHJ4QQjap5\nJHow5ZvcC/DregDG92xNm0AvZq46KtMiCCGcWvNJ9B2vBlePsvKNu6sL91/ZkU2J51m6T1r1Qgjn\n1XwSvacfRF8JBxaDpQV/68B2dGrlx/OL95JfJCNwhBDOqfkkejDlmwuJkLwPMK36v/ymO4nncpi1\nNtGmoQkhRGNpXom+yzjz88Disk1XdgllVLdWvLnsMMmZcrWsEML5NK9EH9Aa2vQpq9OX+tOE7uQX\nFfPKjwdsFJgQQjSe5pXowZRvTm2FjNNlm6JDfLl7SDRfbT3JrpNpNgxOCCEaXvNL9DETzM+DFVv1\nD1/ViWBfD/723V4z3LK4CBY/AcueL+u8FUIIR9T8En2rbtCi/SXlG38vd54c05Wtxy/w3fZfYf69\nsPl9WPUy/PyXy0/2Sdvhk+vg3JEGDF4IIS5f80v0SkHXCXB0JeRnVdg1uW8kvdr44bPoAdizAK75\nB/S/D9a9CSterPt7HF0Js34DR5fDjtkN/AGEEOLyNL9EDxAzDorz4cjSCptddDEfBcxkVMk6VrZ/\nFIY8CuNegvjbYeWLsOb12o+991uYPRkC20J4Tzj0UyN9CCGEqJvmmejbDQavFhXLN8VFsGA6QYmL\n+Cb0AaYfGcyJ8zng4gIT34AeN8Ivz8LGmdUfd+ss+GoatOkNv10CsTfAmd0VOn6FEKKpNc9E7+pm\nFg4/+INJ8JYkT8LXMOpvDLjtOZSCF7/fb57v4grXv2c6cr//H9j2acXjaQ2r/wXfPWamWrhjAfgE\nQefRZv/hX5r28wkhRDnNM9GDKd/kXoDja2HB/WVJnqGP06aFNw9c2ZHFu0+z4eg583xXd7jpI5PI\nFz4Cu+eZ7SUl8OOfYOnfoedNcMsc8PA1+8Jiwb+NlG+EEDbVfBN9p1FmkrOv7oKEeTDqORj6eNnu\n+4d3JKKFNzM+28rSfWfNRjdPmPIZtB8C86ebDttvH4QNb8PAB+D6meYPQimloPM1cGQ5FBc26ccT\nQohSzTfRe/pD9HDTqr/6WRj6uwq7vT1c+ezegbQO9Oaej7fw9+/2UlBUAh4+cOsXENHH1ON3zoGR\nf4axL5p6fmWdR0NBJvy6oWk+lxBCVOJm6wBsatxLkHrQlHGqEB3iy/wHB/Pi9/v5cO0xNiee561b\ne9M+2B9um2dKOJ1GQd+7qn+PDleCi7sp30QPa6QPIoQQ1au1Ra+U+lAplayUSqhi3x+UUlopFWJ5\nPEIpla6U2mG5/bUxgm4wwR2rTfKlvNxdeW5iLDPv6Muv53OY8MYaFu5MAu8WMOXTmpM8mG8O7QfD\noZ8bMHAhhKi7upRuZgFjK29USrUFRgO/Vtq1Wmsdb7n9vf4h2ofRseEseWwYXcP9eXTOdp6at4vc\ngjrOYd95NKTsg7TKp0oIIRpfrYlea70KOF/FrteAJ4FmMxFMRAtvvpg+iIdHdmLu1hNMfGsNR1Oy\nan9h6TBLadULIWzAqs5YpdQk4JTWemcVu69QSu1USn2vlIqt4RjTlVJblFJbUlJSrAnDJtxcXXhi\nTAyf3TOQc9kF3PPxFjLyahlRE9LZzK/jLIl+40z4aLwZWiqEsHuXneiVUj7AM0BV9fdtQHutdS/g\nTeCb6o6jtZ6pte6nte4XGhp6uWHY3JBOIbx3R19OnM/hD3N3UlJSwxcbpUyr/thKKHTwxU2KC83F\nYcfXli20LoSwb9a06DsC0cBOpVQiEAlsU0qFa60ztNZZAFrrJYB7aUetM+ofFcSfJnTj571neWdl\nLbNUdh4NhTkmQTqyA0sg64y5v+tL28YihKiTy070WuvdWutWWusorXUUcBLoo7U+o5QKV0opAKXU\nAMvxzzVoxHZm2uAoJsW34ZWfDrDqYA0lqKih4Obl+OWbzR9AQCT0vBn2fOP431CEaAbqMrxyDrAe\niFFKnVRK3VPD0ycDCUqpncAbwFStnXvVDqUU/3dDT2LC/Hn0i+1mIrSqePhA1DDHng4h9bApP/Wb\nBvG3QH46HPrR1lEJIWpRl1E3t2itW2ut3bXWkVrrDyrtj9Jap1ruv6W1jtVa99JaD9Jar2uswO2J\nj4cb797el+ISzYzZW8krrGbYZefRcP6I4y5GsuVDcHGD3ndC9JXgFwa75to6KiFELZrvFAgNLCrE\nl9dujifhVAZ//TaBKr/IdL7G/HTE8k1hrllEpdu14B9mZvTseRMc/BFyqhp9K4SwF5LoG9Co7mE8\nclUn5m45yZxNJy59QlA0BHd2zPJNwnzIS4N+5Sp3cTdDSSHsrXZwlRDCDkiib2CPj+rC8C6hPLdw\nDztOpF36hM6jIXENFGQ3fXD1sfm/EBJjOpVLhcdBaFcp3whh5yTRNzBXF8UbU+NpFeDJjM+2kpxR\naVRK52vMMobHVtsmQGskbYekbdD/HnNNQCmlTKv+1/VwIdFm4QkhaiaJvhG08PHg3dv7kp5byI3v\nriMxtVzrvf1gcPd1rPLN5g/A3Qd6Tb10X8+bzM/dXzVtTEKIOpNE30h6RATy+X2DyMorYvK760g4\nlW52uHlChxGmQ9YWI0+1htRDsH8xFFQzFLS83DSzmlbPyeAVeOn+Fu3MQiy75trm8wghaiWJvhHF\nt23BvBmD8XRzZerMDaw7nGp2dL4G0n81c+E3tpJiOL0LNrwLX94Br3SGt/rBF7fCp9dDXnrNr9/5\nBRTlVuyErSzuZvNZTu9o2NiFEA1CEn0j6xjqx9czBhPRwptpH21mye7T5YZZNmL5ZtdcmH0T/DMK\n3hsGPzxlEnHHq+HaN+Daf8OpLfDxtZBdzcXLWpux8xF9oU189e/VfZJZllE6ZYWwS5Lom0B4oBdz\n77+CuMhAHvp8G5/uK4ZWsY2X6JP3w/z7TImmxw1ww/vweAI8vhtueM8sltJ3Gkz93Dx31gTIPHPp\ncRLXQOoB6H9vze/n3RK6jDUlnuKiRvlIZfZ+Cx+MgfRTjfs+zubUNnipg+NerCfqRRJ9Ewn0cefT\newZyVUwr/vJNApvd+6KPr4NN70N+Hea0vxzr3wI3b7h3qWm5x90MLdpe+rwuY+D2eWZBlI/GXbow\nypYPwKsFxF5f+3vGTYHsZDi2okE+QpW2fWrW6T2xAX76U+O9jzPavxhyzpkF7ZuTwjzzTTN5n60j\nsSlJ9E3I28OV9+7oy019I3nsSD9OeXaGJU/Aa93hp79AWhUXWV2urGTzHzv+FvANrv350cPhzm9M\n+ebDcRdbfJlnYN930Pt2cPeu/TidrzF/FHY20oyW6/8DCx82HdlDHjcJ6+jKxnkvZ5S4xvw8sMS2\ncTSVvHRY/Sr8O858u130O1tHZFOS6JuYm6sLL02OY+KVAxl64c881eIVsiOHw/q34d+9YO6d8OsG\n60ewbP6vGac/6MG6v6btAJj2nel0/XAsnN1rWs8lRdDv7jp+ME/T8t+/qPZvKPmZZl77utAalv8f\n/Pg0dJsIt3wBI/5oFnL5/sm6H6c5K8iGU1vNqKlTWyHjtK0jajyZZ+Dnv8JrPWDp3yAs1sy0+uv6\nZl3uk0RvA0op/jiuK/+5rS/fp7Vj4KE7+GHUDzD4YTi6Aj4cA++PhAPfX96BC3NNou8yzqxqdTla\n94JpS0C5wKzxsPl903oO7lj3Y8RNMXPu719c9f60X+G7x+Cf0fB6HKx9A/Iyqj9eSQn88DSsfBHi\nb4fJH5k/KO7eMPZFSNkPm2Zezqdsnk5sMlNVDP8f8/jgZf6/cgSph2HhI/B6T1j3pvmGef8quGOB\naRhA8ytblSOJ3obG92zN948Pp3vrAB74LoXHz11P5oO7YMKrptX7xW1wuqrVGqux8wtThx38sHUB\nteoKd38PHv6Qdbb2TtjK2g404+orL0hSmuDf6AM7Pof4WyGkE/z8F3gtFn5+9tJWZnGRKdVsfMd8\nO5n4Jri6XdwfMw46XWNa+5lnrfu8zUXiGlCu0Pe30DIK9jtZ+WbN62bI8K650PsOeGQrTP7QNF7A\nNFZax0PC17aN04Yk0dtYRAtvPr9vIL8b1YWFO5OY8O52tofdAPf+Aj7BsPBRMxa+NiUlpvzTOt5c\nwGStoA5wz4+mEzdm/OW91sXFfE0+utwk3wvHTfxv9DYJvu80eHQHTHwD7voO7lsOna6GdW+Ylti3\nD0HKASjKh3nTzGyZI56BMf9rjl2eUjDun6ZM9cuz1n/eyo6uhPNHG+549iBxDUT0AU8/iJlg1hTI\nz7R1VA0jLwNWvQwdrzIjy37zqvk/XFmPG800Hs72b1tHkujtgJurC4+N6szc+6+guERz07vreXvD\nOUrGvmjGvm98r/aDHPoJzh2CKx6uOB+NNQLamKTs4nr5r427GXQJfH4zvNkHds4xLclHd8CEVyAw\n4uJzI/rATbPgkW3m/XZ/DW8PgDf7mY7gsS/CiKeq/zzBHc3n3TnH9GvUV24afHaD6ZR2lnpuaX2+\ndDK6ruOhuAAOL7VtXA1lx+dQkAVX/Qn8alh7unTkWML8ponLzkiityP9ooJY8tgwxvQI5+UfDzB1\nbWty2l8Ny56vfUTO+rcgIAJir2uaYKsTGgOR/SF5r+nIrSrBVxYUbZ7zuz0w4mlTopn0NgyaUfv7\nDX/CfO4lT9Ttm09NDv1sOqBzzsGcKQ0/7NUWSuvzpYm+7SBz3YMzjL4pKYFN70HkAHNRX01atDWf\n3Z4Svdaw7q0mKaVJorczgd7uvHVLb16aHMe+M5mMP3IdhcXFlCz6ffUjcZJ2QOJqGPgAuLo3bcBV\nuXUu/H4fjH+55gRfmW+w6Th7dLsZ1lkXHr4w+nk4sxu2fmRdvKX2f2dWzZr6OZzdA/On1/+Ph62V\n1ufbDjKPXd3MxW0Hf2z8i9sa2+FfTClm4P11e36PGyF5j7lI0NZKis2osZ/+BPsWNvrbSaK3Q0op\nbu7XlqW/v5LYbj14MX8yLod/4sjK2VW/YP3b4OFnrni1Bz5B4BvSdO8Xe71Zj3fpP6qfzqE2hXlw\n6BfTL9FlNIz9JxxY3LD1f1tIXH2xPl8qZrxZRObX9baLqyFseg/8ws2w27roPsmMKttj41Z9QY6Z\nd2rTTFN6nPSfRn9LSfR2rFWAF2/f1ofBtzzDPtWRgOXP8MK8dWTklRs7nn7K/Mftc2fVs0s2B0qZ\nbw/5mbBjRN/uAAAcp0lEQVTs79Yd49hKKMyGrr8xjwdOh/73maF6Wz9uuFibUuX6fKmOV4Grp2OX\nb1IPmRZ9/3vAzaNur/EPM+ci4WvbzbSalQIf/8ac+3EvwZgXLh1o0Agk0TuAq3tE0H7afwlWWUTv\nfJlrXl3JDwmWuWk2vWc6Pwc+YNsgba1VN3MOtn5s5nW5XPsXmWGl0cMubhv7InQaBYt/3zRX4e5d\naEaQNNR8NCc2mj6Hyone0w86XGmud3DUqaU3zTQT6fWddnmv63EjnDsMZ3Y1Slg1Sj0EH4wyFyRO\nnV33klMDkETvIHza98Fl8IPc6rqMYR6HeOCzrfz+szXoLR+Zr6Qt29s6RNsb8RT4hpra5+UksJJi\nc3Fa52vMBVmlXN3MeOzgzjD3DvOL2lgO/2Lm8Vn2vBmt9N9RZh6k7FTrj1m5Pl9ezHhIO246zR1N\nXoYZbRN7A/i1urzXdpsILm5NP6b++Hr44BrTwT9tEXSd0KRvL4nekYx4Glq042XPD3nqmmha7P8C\nlZ9BUvfLvLDJWXkFwsin4eRmOL6u7q87uRmyU6r+5fMKhFu/ABd3M+1zzvmGi7fU2b3w1W/Nt5KH\nNsE1fzdXOS95Av4VA59PMYmpLgvFlFd+/HxlMePMT0e8eKp0SOXA6Zf/Wp8gU7pKmN9032b2LIBP\nJoF3ENz7M0T2a5r3LUcSvSPx8IUJr6HOHWSGms+TgcvZTgxjv8pm+YFkW0dnH+KmmuGDG9+t+2v2\nLzKJvPPoqve3jIJb5kBGEnx5u+m4bShZySaRu/vArV+a4alDHoMZa+GBtXDFQ2bhmHl3wytd6j5p\nXHX1+VL+4RDRz3Q421LmGVj8B3i5k+kMr03ZkMr+tQ+prE6PGyH9hPkD35gK82DZC+abWpve5iLI\nqi7magKS6B1N51FmndZVL+OVfZK2E54ksqUPd8/azFvLDqEdtebaUDx8TMf0/kV1mw1Ua9i3yNSs\nvQKqf17bAXDdf+D4WnNR1+55JunUR2EuzLkFclLNt4bAyIr7w3uY1v3vEsyVxMEd4cdnTBKvTXX1\n+fK6jjcLv2ck1e9zWCPnvJl87N/xsHWW6Rz+8nZT4qjJkaWWIZX16JOKGW/erzHLN4d+hv8MglUv\nQa9b4M5vzbcJG5FE74jG/J+ZErhlNCF9r+frGYOZ2KsNr/x0kBmfbSMr38HHR9dX6Rw9m/9b+3OT\n98GFY3WrmfacDHd8A54B8PU98N+r4Nhq62IsKYEFD5hW9w3vmxZfdVxczXTS4/5p/ihsnVX78Wuq\nz5eKsXzmphx9k58JK182M7WufQO6T4SHN8P0FeYP3ec31zy/08Z3L29IZVW8Akx/zJ4FDX+dRNqv\nZo6q2ZPNNS13fgvXvwvuXg37PpdJEr0j8gs1i4rcsQBcXPH2cOX1KfH8eUI3ftp7huveXsvRFCe4\nqtNaLdqZxL3tY9NqrknpTJt1nden40gzK+J1714cKvf5lMtf2GL587D3G9Ni7/abur2m3SBzvcDa\nN2ovH9VUny8VGmNKCU1Rpy/MM2sK/DvefPbo4TBjHdww08TgF2rWRfAKhE9vqLrju3RIZb+76z6k\nsjo9bjQT911OX05NivJh1Svw1gA4sgxGPWdKbx1GNMzx68mt9qcIuxTSqcJDpRT3DutA99YBPPT5\nNia9tZZruofh6e6Kp5sLnu4ueLqZ+17urvh4uDK0Uwhtg3xs9AEa2cAHzHw5u78ypZzq7F9k6r3+\n4XU/touLWdgl9jrTwlz9Krwz2FzNO+IZCGhd8+u3z4bV/4I+d8HgR+r+vmCmGv5kImz/FAbcV/Vz\nSuvztR1bKfMHbuN7ZiRLTaWr+sg+Z779XEg0ie+qv0JkFfX1wEjTAv5wDHxyHdz9Q8WV0TbNNH0p\n/X5b/5i6jAF3X1O+KT+k1hpHlsGS/zHDNrtNNJPwVbWimw0pe6jp9uvXT2/ZssXWYTiNkxdyeHr+\nbo6mZJNfVEJ+UTH5RSUUFF1aUx4QFcR1vSOY0LM1gT52MH1CQ9Ea3rXUpx9YU/XEaOknzTTJo/4G\nQx+3/r2yz8HqV8xwSDC19da9Lt5axV786n5sNXx6PUQNgdvmXf6UFVqbRJh+ykwVUVXL9sgy8x63\nf22uA6jJ8XVmGcnJH5n1hauSl26Gfbp6mOkmLnfSvG8fMlNo3/Kl6WOqzZnd8NEEc3X13T+YIZR5\nGfBqN3NB2w11mOSvLubdY87VEwetnzpk0/tmdFRQBxj3ct0+XwNSSm3VWtc6jEda9E4osqUPn94z\n8JLtJSWaguIS8otKOJeVz/cJZ5i/7STPLNjNcwv3cFXXVlzXO4KRXUPxdLNi5kp7opS5IGXhI6YD\ntapOydKSRdc6lk6q4xsMY//PtLC3zjIdnHsWXKylu7hBaDdoHWdKRUEd4KaPrUsuSsHwJ2H2jZaZ\nQauY9qIu9flSbQea6bAPLKk60R/4ARY9DpmW9QIi+piyR139ugG2f2ZGEtU1CYb3hNu+gk+vM2Wc\naYvMHwprh1RWp8eNkDDPXAxnTYLOSoalfzffUm750uZ1+JpIi76Z01qzJymD+dtOsXBnEqlZ+QR6\nuzOxVxseH9WZYD/P2g9irwpzTSswaihM+ezS/R9fa+bNf3hTw7+31uaCpNM7zaRzp3eaKac9/Ex5\nIii6fsd+fyTkXoCHt1ZckAXgg9Hmaul76zBcEeCbB00J63+OXPzjk30OfnjKlL5adYdr3zCPzx+D\nhzbW7UKl4iJ4b7j5RvDQxpr7C6pyeKnp/4joY65z8Amu+2eqi6J8eLmz6c+5/p3Lf/03D5rFTh7c\ncEkptanUtUUvnbHNnFKKHhGB/PXa7mx4+io+vnsAI2NC+XLzCUa/toolux14fVF3b3OJ/P7FZjRE\neTnnIXFt412hqJQZf999Eox6Fu6YbxLpYzvrl+RLjz38SVPz3v1VxX21jZ+vSsx4k4yPrzV/RBLm\nmyGkexaYi/Smr4S2/c3kWwVZZkqIujQQN71nZosc9+LlJ3kwi9JM/sCMd6/vkMqquHlCt2vNH7nL\nvTbixCazMM4VD9ksyV8OSfSijJurC1d2CeX1qb357pGhRLT05sHZ23ho9jbOZeXbOjzr9LsHUJcO\ntTz0E+ji+pdtLodS9V8UplTMOAjrafoGyg8RrMv4+co6jgQ3L1Ni+fJ2mPdb05l4/yozbXRpP0Cr\nrjDyGdPJXdsMkBlJsPx/ofOY+p3j7pPg+pnmZ32GVFanxw2Qn3F5UwWXFJvOV//WF9fhtXOS6EWV\nYsL9mT9jMP8zJoaf957lmtdWsXiXA7buW7Q1wxe3flxxCoH9i8C/Tc3j1+2ZUmbRlXOHKy56fTn1\n+VIevtBhpPl2cOhn0zl9zy8QFnvpc694xFyRuvgJU6Ouzg9Pmz844/5Z/z9ucTfBzZ/Uf0hlVaKv\nNH0Ci35vOoHrYtsnpgw3+nnrvqnYgCR6US03VxceGtmJ7x4ZSmRLbx763EFb9wMfMPOv755rHhfm\nmvpv1/FNMkVso+k2EUK7mvHbpVfp1mX8fFUGP2I6J2esNSOQKtf9S7m61V7COfyLuUZg2BP1L1M1\nNlc3s1COVwDMvrn2JSRzzpsO2PZDLq9T2sYc+H+5aCpVte7nbT1JXqGDrL7U7gpT5tj4nklMR1dA\nYU6TzyDY4FxcTDJN2We+oVhTny8VNcTM1BnSufbn1lTCKcwzZY2gjjDk0cuPwxYC2phRPgVZZuK6\nvPTqn7v8BdNoGPdSw5XhmoAkelEnpa37RY8OpW1Lb574aid9//Ezv/tyB8v2n61yjL7dKB1qmbzX\nrLi0fxF4BkJ7KxKivelxg0mqq142Qxkvtz5vrepKOGv/bTpOJ7xSccpnexcWa8pDqQdg7p1QVHDp\nc07vgi0fmik2wns0fYz1UKdEr5T6UCmVrJRKqGLfH5RSWikVYnmslFJvKKUOK6V2KaX6NHTQwna6\nhPkz/8EhzL53INf2asOy/cncPWsLA/73F56ev4t1h1MpLrH9kN1L9Jxspold/7aZe77L6Map+TY1\nF1cY9gezkMayf1x+fd5aVZVwzh81V/zG3mCmAnY0HUfCxDfNN77vHqtYltLafFPxbmm+zTiYul4w\nNQt4C/ik/EalVFtgNFB+7No4oLPlNhB4x/JTOAlXF8WQTiEM6RTC3yf1YM3hFL7beZqFO5KYs+kE\nIX6ejIkNY2RMKwZ3CsbHww6uyysdarnmVfO4KUfbNLa4m2Hli+ZCrcj+TddBWFrC+eU5U8LZ8bm5\nenbM/zbN+zeG+FvNUNwV/2cW8xnxR7N911w4scFcT+Dd0rYxWqFOv4Fa61VKqagqdr0GPAl8W27b\nJOATba7E2qCUaqGUaq21dsAhG6I2Hm4uXNU1jKu6hpFXWMzy/cks3JnEgu2nmL3xVzxcXRjYIYgR\nMa0YGRNKdIgvyla1zf73mNKCi5sZo+0sXN1h6O9g0e+apmxT3hWPmFr9Nw9CUZ5ZfrG2uX7s3ZVP\nXUz2gW3NWPuf/wJt+kDvO2wdnVWsbmoppSYBp7TWOyv94kYA5ScCP2nZViHRK6WmA9MB2rVrZ20Y\nwo54ubsyrmdrxvVsTX5RMVsSL7B8fzIrDqbwj0V7+cciaBfkw8iYUCb1jqBPuyZuGQVGmhE4JUXg\n6d+0793Y4m8za83WNIFbYygt4bw3zHR4969mojVHohRc+29zLcB3j5qJz7LOwtTPHXaUVp2nQLC0\n6BdprXsopXyA5cBorXW6UioR6Ke1TlVKLQJe1FqvsbxuKfCU1rraOQ5kCgTnd+J8DisOJLP8QArr\njqSSV1hCfNsW/HZIFON7tsbd1TF/gYTF6V1mBtDLXcPVnuVlmAnfziaYmUknvW3riC5R1ykQrE30\nPYGlQOkVKJFAEjAA+BuwQms9x/K6A8CImko3kuibl+z8Ir7edpKP1iZyLDWb8AAv7riiPbcOaEdL\nXyfoIBXOIyMJNvwHhvzOTF5nZxo10VexL5GLLfoJwMPAeEwn7Bta6wE1HVsSffNUUqJZcTCZD9ck\nsuZwKp5uLtzQJ4K7BkcRE+Zvu1q+EA6iQacpVkrNAUYAIUqpk8CzWusPqnn6EkySP4xp8TfAKgHC\nGbm4qLKO3ANnMpm17hjzt51izqYTeLi6EOLnQai/J6H+noT4XfzZpoU3QzuF4O3h4FMpC9FEZJpi\nYVfOZxeweFcSJ9NyScnMJzWrwPIzn3NZ+ZQO0ff1cGVMj3Cu7x3B4I4huLpI6180P7LwiHBIQb4e\n3HFFVJX7iks0F3IKOHgmk4U7k1i8+zTzt52ilb8nk+LbcF3vCLq3DpCSjxCVSIteOKy8wmKW7U9m\nwfZTrDiQTGGxpkuYHzf3a8sdV7R3/FWyhKhFg3fGNiZJ9KK+LmQXsHj3aRZsP8XW4xeICvbh2Ymx\njIxxouF+QlQiK0yJZqWlrwe3D2rP1zMG88ndA3BxUfz2o83c+/EWfj2XU/sBhHBikuiF0xneJZQf\nHhvO0+O6su5IKqNeW8mrPx8kt8BBplUWooFJohdOycPNhfuv7MiyP4xgXI9w3lh6iFGvruSHhDPY\nQ7lSiKYkNXrRLGw4eo7nFu5h/5lMOoT4EhXiS9uW3rQN8iGypQ9tg8z9AC93W4cqRJ3J8EohyhnU\nIZhFjwxlzuYTrDmUwonzuWw+dp7M/KIKzwv0dufKLqHcN6wDPSMDbRStEA1LWvSi2dJak55byInz\nuZy4kMOJ8zkcTclm8e7TZOUXMTA6iPuGdeCqrq1wkQuyhB2S4ZVCWCkjr5AvN53go7XHSErPo0Oo\nL/cMjebGPpF4uVccm6+15nx2AYnnsjmWmkNaTgHXdA+jfbCvjaIXzYkkeiHqqbC4hCW7T/Pf1cfY\nfSqdIF8PpvZvi5uL4ti5HBJTs0k8l01mXtElrx3WOYSp/dtxTfcwPNxkzINoHJLohWggWms2HjvP\n+6uOsnR/Mi4KIlp6ExXsS3SIL1HBvkSF+BAV7Iu7qwsLtp/iy80nOJWWS7CvB5P7RXJL/3ZEhUgr\nXzQsSfRCNIIL2QX4eLrWOr1CcYlm1aEU5mz8laX7kyku0QzpFMx18RH0iAikQ6ivTNEg6k0SvRB2\n4mxGHl9tOcGcTaaVD2aB9egQX2LC/Okc5kdMmD9dwv2JCvaVmThFnUmiF8LOFJdoDidnceBsJgfP\nZHLgbCaHzmZy/HwOpb+GQb4eTOjZmknxbejTrqWM9hE1knH0QtgZVxdFTLg/MeH+0Ovi9tyCYg4n\nZ7H/TAYrDqbw1dYTfLrhOBEtvLm2Vxsm9mpDt9ay4pawnrTohbAzWflF/Lz3DN/uSGL1oVSKSzSd\nW/kxKb4No2PD6dzKT5K+AKR0I4RTOJeVz5KEM3y3I4lNiecBaOXvydBOIQyx3MIDvWwcpbAVSfRC\nOJnT6bmsOpjCmsPnWHs4lfPZBQB0auXH0E4hXNExGC93V3ILisjOLyanoIicgmKyC4rJyS8ip7CY\nwqISCotLKCzWFBSX3i+hsEgT4O3GbYPaM6JLqHxjcBCS6IVwYiUlmn1nMlh7OJU1h8+x6dg58gpL\nqn2+j4cr3u6ueLq54O7mgruruXm4Kjwsj4+kZHE2I5+YMH+mD+/Atb3ayMVedk4SvRDNSF5hMXuS\nMgCNj4cbPh6u+Hi44evpipeba51G7xQUlfDdziRmrjrKgbOZhAd4cffQKG4Z0A5/mdXTLkmiF0JY\nRWvNioMpzFx5lPVHz+Hv6catA9sxIa41Ph7mYjFPNxfz090FD1cXGQZqI5LohRD1tutkGjNXHWXJ\n7tOU1JAqPFxd8PF0xd/LDT9Pd/y93AjwcsPfyx0/Tzf8vdwI9HanhY87gd4etPAx91tY7leeLE7U\njYyjF0LUW1xkC966tQ8nzuew93QGBUUl5BeVkF9UTH7hxft5hSXkFBSRmVdEZl4hGXlFJKXlkZmf\nadlWRHENfyl8PVzpHx3E0E4hDO8SKkNIG5gkeiFErdoG+dA2yMfq12utyS4oJi2ngLScQtJzC0nL\nKSQtt4D03EKS0nJZd+Qczy/eB4v3ERbgydBOoQzrbIaQhvp7NuCnaX4k0QshGp1SCj9PN/w83Yhs\nWf3zTqXlsuZQCqsPpbJs/1m+3nYSgLjIQG4b2I5J8RFS5rGC1OiFEHappESzJymDVYdS+G5nEvvP\nZNLCx50p/dtyx6D2RLa0/huGs5DOWCGE0yhdE+DjdYn8tPcsWmtGdQtj2pAorugQ3Gzr+dIZK4Rw\nGkopBnUIZlCHYJLScvlsw3HmbPqVn/aeJSbMnzE9wgnwMqUhPy83fD3d8Pc0P/083QgL8LL64q+i\n4hIy84pIzy0kI8/0L2TkXnzs4+HKzf3a2nVJSVr0QgiHlFdYzMKdSXyyPpGEUxk1PtfDzYXurQOI\nb9uCuMhAerVtQXSw7yXj/7Pzi9h7OoPdJ9NJSEon4VQ6h5OzahxaCtAx1JeXJveib/saOiAagZRu\nhBDNRlFxCdkFxWTlF5GdX3TxZ14RmflFHEnOYseJNHafSienoBgAfy834iIDiW0TyNmMPBJOpXM0\nNbtsbYAQP096RgTQrXUAIX6eBHq7E+jtToDlp7nvxpbECzw9fzdJ6bncMySaP4yOwdujaVr3kuiF\nEKKS0sVfdp5MY+eJNHaeTGP/6UxC/T2JbRNIz4hAekQE0CMikLCAus8KmplXyIvf72f2xl+JCvbh\npcm9GBAd1IifxJBEL4QQdVBSohtsCod1h1N5av4uTl7I5a4ronhybAw+Ho3XFSqdsUIIUQcNOU/P\n4E4h/Pj4cF764QAfr09k6f6zPHpVZ8ICvMpNAeGOv5d7k64NLC16IYRoBJsTz/PkvF0cS82+ZJ9S\n4O/pRqCPO2O6h/Pn33S36j2kRS+EEDbUPyqIn343nMTUbNJzL077kJ5bSFpuIRm5haTlFNC6hXej\nxyKJXgghGom7qwudw/xtHQayfIwQQji5WhO9UupDpVSyUiqh3LZ/KKV2KaV2KKV+Ukq1sWwfoZRK\nt2zfoZT6a2MGL4QQonZ1adHPAsZW2vay1jpOax0PLALKJ/TVWut4y+3vDRSnEEIIK9Wa6LXWq4Dz\nlbaVv97YF7D90B0hhBBVsrpGr5R6QSl1AriNii36K5RSO5VS3yulYmt4/XSl1Bal1JaUlBRrwxBC\nCFELqxO91vpPWuu2wGzgYcvmbUB7rXUv4E3gmxpeP1Nr3U9r3S80NNTaMIQQQtSiIUbdzAZuBFPS\n0VpnWe4vAdyVUiEN8B5CCCGsZFWiV0p1LvdwErDfsj1cWVYAUEoNsBz/XH2DFEIIYb1aL5hSSs0B\nRgAhSqmTwLPAeKVUDFACHAcesDx9MjBDKVUE5AJTdR3mWNi6dWuqUuq4dR8BgBAgtR6vb0wSm3Uk\nNutIbNZx1Nja1+UAdjHXTX0ppbbUZb4HW5DYrCOxWUdis46zxyZXxgohhJOTRC+EEE7OWRL9TFsH\nUAOJzToSm3UkNus4dWxOUaMXQghRPWdp0QshhKiGJHohhHByDp3olVJjlVIHlFKHlVJ/tHU85Sml\nEpVSuy3TNdt0ncRqppoOUkr9rJQ6ZPnZ0o5ie04pdarcdNfjbRRbW6XUcqXUXqXUHqXUY5btNj93\nNcRm83OnlPJSSm2yzHm1Ryn1N8v2aKXURsvv65dKKQ87im2WUupYufMW39SxlYvRVSm1XSm1yPK4\n/udNa+2QN8AVOAJ0ADyAnUB3W8dVLr5EIMTWcVhiGQ70ARLKbXsJ+KPl/h+Bf9pRbM8BT9jBeWsN\n9LHc9wcOAt3t4dzVEJvNzx2gAD/LfXdgIzAImIu5iBLgXWCGHcU2C5hs6/9zlrh+D3wOLLI8rvd5\nc+QW/QDgsNb6qNa6APgCMx2DqERXMdU05lx9bLn/MXBdkwZlUU1sdkFrfVprvc1yPxPYB0RgB+eu\nhthsThtZlofulpsGrgLmWbbb6rxVF5tdUEpFAhOA/1oeKxrgvDlyoo8ATpR7fBI7+Y9uoYGflFJb\nlVLTbR1MFcK01qct988AYbYMpgoPW1Yx+9BWZaXylFJRQG9MC9Cuzl2l2MAOzp2l/LADSAZ+xnz7\nTtNaF1meYrPf18qxaa1Lz9sLlvP2mlLK0xaxAa8DT2KmlwEIpgHOmyMnens3VGvdBxgHPKSUGm7r\ngKqjzXdCu2nVAO8AHYF44DTwL1sGo5TyA74GHtcVF92x+bmrIja7OHda62JtVqCLxHz77mqLOKpS\nOTalVA/gaUyM/YEg4Kmmjksp9RsgWWu9taGP7ciJ/hTQttzjSMs2u6C1PmX5mQwswPxntydnlVKt\nASw/k20cTxmt9VnLL2MJ8D42PHdKKXdMIp2ttZ5v2WwX566q2Ozp3FniSQOWA1cALZRSpRMp2vz3\ntVxsYy2lMK21zgc+wjbnbQgwUSmViClFXwX8mwY4b46c6DcDnS090h7AVGChjWMCQCnlq5TyL70P\njAYSan5Vk1sI3GW5fxfwrQ1jqaA0iVpcj43OnaU++gGwT2v9arldNj931cVmD+dOKRWqlGphue8N\nXIPpQ1iOmeEWbHfeqoptf7k/3ApTA2/y86a1flprHam1jsLks2Va69toiPNm6x7mevZOj8eMNjgC\n/MnW8ZSLqwNmFNBOYI+tYwPmYL7GF2JqfPdgan9LgUPAL0CQHcX2KbAb2IVJqq1tFNtQTFlmF7DD\nchtvD+euhthsfu6AOGC7JYYE4K+W7R2ATcBh4CvA045iW2Y5bwnAZ1hG5tjqhpkavnTUTb3Pm0yB\nIIQQTs6RSzdCCCHqQBK9EEI4OUn0Qgjh5CTRCyGEk5NEL4QQTk4SvRBCODlJ9EII4eT+H1qXlbKc\n/yyVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa96aaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa93786a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8555a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for (hist, gp, ep) in zip(hists, gp_list, epochs_list):\n",
    "    if ep:\n",
    "        plt.plot(hist.history['k_smape'], label='train')\n",
    "        plt.plot(hist.history['val_k_smape'], label='validation')\n",
    "        plt.title('SMAPE - ' + str(gp))\n",
    "        plt.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict for Input Set (Training Set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 0: 0 1 2 3 \n",
      "Run 1: 0 1 2 3 \n",
      "Run 2: 0 1 2 3 \n",
      "Run 3: 0 1 2 3 \n",
      "Run 4: 0 1 2 3 \n",
      "Wall time: 1min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Y_input_pred_list_ens = []\n",
    "for models, run in zip(models_ens, ens_list):\n",
    "    print('Run', run, end=': ')\n",
    "    Y_input_pred_list = []\n",
    "    for (gp, model, X, A) in zip(gp_list, models, X_input_list, A_input_list):\n",
    "        print(gp, end=' ')\n",
    "        Y_input_pred_list.append(model.predict([X, A]))\n",
    "    Y_input_pred_list_ens.append(Y_input_pred_list)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_index_range = np.arange(len(input_gp)); #input_index_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# list for original index\n",
    "input_index_list = [input_index_range[input_gp == gp] \n",
    "                     for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27413,) (40041,) (76815,) (794,) "
     ]
    }
   ],
   "source": [
    "for o in input_index_list:\n",
    "    print(o.shape, end=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_index_list_comb = np.concatenate(input_index_list); #input_index_list_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_input_pred_ens = []\n",
    "for Y_input_pred_list in Y_input_pred_list_ens:\n",
    "    Y_input_pred_comb = np.concatenate(Y_input_pred_list)\n",
    "    Y_input_pred = [0]*len(input_index_list_comb)\n",
    "\n",
    "    for index, y in zip(input_index_list_comb, Y_input_pred_comb):\n",
    "        Y_input_pred[index] = y\n",
    "\n",
    "    Y_input_pred = np.array(Y_input_pred)  # make it an numpy array (which will also make a copy)\n",
    "    Y_input_pred_ens.append(Y_input_pred)\n",
    "\n",
    "del Y_input_pred_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inverse transform Y_input_pred to original Y\n",
    "Y_input_pred_ori_ens = []\n",
    "for Y_input_pred in Y_input_pred_ens:\n",
    "    Y_input_pred_ori = unlog(untransform(Y_input_pred, Y_input_center, Y_input_scale))\n",
    "    Y_input_pred_ori_ens.append(Y_input_pred_ori)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_input_pred_ori_ens = np.array(Y_input_pred_ori_ens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict for Output Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 0: 0 1 2 3 \n",
      "Run 1: 0 1 2 3 \n",
      "Run 2: 0 1 2 3 \n",
      "Run 3: 0 1 2 3 \n",
      "Run 4: 0 1 2 3 \n",
      "Wall time: 44.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Y_output_pred_list_ens = []\n",
    "for models, run in zip(models_ens, ens_list):\n",
    "    print('Run', run, end=': ')\n",
    "    Y_output_pred_list = []\n",
    "    for (gp, model, X, A) in zip(gp_list, models, X_output_list, A_output_list):\n",
    "        print(gp, end=' ')\n",
    "        Y_output_pred_list.append(model.predict([X, A], batch_size=4096*2))\n",
    "    Y_output_pred_list_ens.append(Y_output_pred_list)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the original index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# list for original index\n",
    "output_index_list = [np.arange(len(output_gp))[output_gp == gp] for gp in gp_list]\n",
    "output_index_list_comb = np.concatenate(output_index_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_output_pred_ens = []\n",
    "for Y_output_pred_list in Y_output_pred_list_ens:\n",
    "    Y_output_pred_comb = np.concatenate(Y_output_pred_list)\n",
    "    Y_output_pred = [0]*len(output_index_list_comb)\n",
    "\n",
    "    for index, y in zip(output_index_list_comb, Y_output_pred_comb):\n",
    "        Y_output_pred[index] = y\n",
    "\n",
    "    Y_output_pred = np.array(Y_output_pred)  # make it an numpy array (which will also make a copy)\n",
    "    Y_output_pred_ens.append(Y_output_pred)\n",
    "\n",
    "del Y_output_pred_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inverse transform Y_output_pred to original Y\n",
    "Y_output_pred_ori_ens = []\n",
    "for Y_output_pred in Y_output_pred_ens:\n",
    "    Y_output_pred_ori = unlog(untransform(Y_output_pred, \n",
    "                                          Y_output_center, Y_output_scale))\n",
    "    Y_output_pred_ori_ens.append(Y_output_pred_ori)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_output_pred_ori_ens = np.array(Y_output_pred_ori_ens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMAPE Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# SMAPE scores for the model\n",
    "if test_length:\n",
    "    model_smape_ens = []\n",
    "    model_smape_0_ens = []\n",
    "    for Y_output_pred_ori in Y_output_pred_ori_ens:\n",
    "        model_smape = smape(Y_output_raw, Y_output_pred_ori) # SMAPE score excluding nan (Correct one)\n",
    "        model_smape_0 = smape(Y_output_ori, Y_output_pred_ori) # SMAPE assuming nan == 0\n",
    "        \n",
    "        model_smape_ens.append(model_smape)\n",
    "        model_smape_0_ens.append(model_smape_0)\n",
    "    model_smape_ens_mean = np.mean(model_smape_ens)\n",
    "    model_smape_0_ens_mean = np.mean(model_smape_0_ens)\n",
    "    \n",
    "    model_smape_stdev = np.std(model_smape_ens)  # standard deviation of the smape score\n",
    "    model_smape_0_stdev = np.std(model_smape_0_ens)  # standard deviation of the smape score\n",
    "    \n",
    "    model_smape_ens_mean_std = model_smape_stdev / np.sqrt(len(model_smape_ens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length:\n",
    "    for model_smape, model_smape_0 in zip(model_smape_ens, model_smape_0_ens):\n",
    "        print('%6.3f' % model_smape, '       ', '%6.3f' % model_smape_0)\n",
    "    print('-----------------------------')\n",
    "    print('%6.3f' % model_smape_ens_mean, '+-%5.3f' % model_smape_stdev, \n",
    "          '%6.3f' % model_smape_0_ens_mean, '+-%5.3f' % model_smape_0_stdev)\n",
    "    print('+-%5.3f' % model_smape_ens_mean_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SMAPE score for Fibonacci median\n",
    "if test_length:\n",
    "    fib_smape = smape(Y_output_raw, Y_output_fib)\n",
    "    print(fib_smape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "esmape = smape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Estimated SMAPE scores for the model using training set\n",
    "model_esmape_ens = []\n",
    "model_esmape_0_ens = []\n",
    "for Y_input_pred_ori in Y_input_pred_ori_ens:\n",
    "    model_esmape = esmape(Y_input_raw, Y_input_pred_ori) # SMAPE score excluding nan (Correct one)\n",
    "    model_esmape_0 = esmape(Y_input_ori, Y_input_pred_ori) # SMAPE assuming nan == 0\n",
    "    \n",
    "    model_esmape_ens.append(model_esmape)\n",
    "    model_esmape_0_ens.append(model_esmape_0)\n",
    "model_esmape_ens_mean = np.mean(model_esmape_ens)\n",
    "model_esmape_0_ens_mean = np.mean(model_esmape_0_ens)\n",
    "\n",
    "model_esmape_stdev = np.std(model_esmape_ens)  # standard deviation of the esmape score\n",
    "model_esmape_0_stdev = np.std(model_esmape_0_ens)  # standard deviation of the esmape score\n",
    "\n",
    "model_esmape_ens_mean_std = model_esmape_stdev / np.sqrt(len(model_esmape_ens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.099         42.530\n",
      "39.394         42.853\n",
      "39.057         42.599\n",
      "39.262         42.757\n",
      "39.431         42.772\n",
      "-----------------------------\n",
      "39.249 +-0.151 42.702 +-0.119\n",
      "+-0.067\n"
     ]
    }
   ],
   "source": [
    "for model_esmape, model_esmape_0 in zip(model_esmape_ens, model_esmape_0_ens):\n",
    "    print('%6.3f' % model_esmape, '       ', '%6.3f' % model_esmape_0)\n",
    "print('-----------------------------')\n",
    "print('%6.3f' % model_esmape_ens_mean, '+-%5.3f' % model_esmape_stdev, \n",
    "      '%6.3f' % model_esmape_0_ens_mean, '+-%5.3f' % model_esmape_0_stdev)\n",
    "print('+-%5.3f' % model_esmape_ens_mean_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.3282\n"
     ]
    }
   ],
   "source": [
    "# SMAPE Est score for Fibonacci median\n",
    "fib_esmape = esmape(Y_input_raw, Y_input_fib)\n",
    "print(fib_esmape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prediction Y_input in group\n",
    "Y_input_pred_ori_list_ens = [[Y_input_pred_ori[input_gp == gp] for gp in gp_list]\n",
    "                              for Y_input_pred_ori in Y_input_pred_ori_ens]\n",
    "# Prediction Y_output in group\n",
    "Y_output_pred_ori_list_ens = [[Y_output_pred_ori[output_gp == gp] for gp in gp_list]\n",
    "                              for Y_output_pred_ori in Y_output_pred_ori_ens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fibonacci median in group\n",
    "Y_input_fib_list = [Y_input_fib[input_gp == gp] for gp in gp_list]\n",
    "Y_output_fib_list = [Y_output_fib[output_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# True Y_input in group\n",
    "# untransformed Y_input with NaN replaced by 0\n",
    "Y_input_ori_list = [Y_input_ori[input_gp == gp] for gp in gp_list]\n",
    "# untransformed Y_input_ori with raw NaN (for SMAPE estimation)\n",
    "Y_input_raw_list = [Y_input_raw[input_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# True Y_output in group\n",
    "if test_length:\n",
    "    # untransformed Y_input with NaN replaced by 0\n",
    "    Y_output_ori_list = [Y_output_ori[output_gp == gp] for gp in gp_list]\n",
    "    # untransformed Y_input_ori with raw NaN (for SMAPE estimation)\n",
    "    Y_output_raw_list = [Y_output_raw[output_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input set group SMAPE: [ 70.25056458  40.66552734  29.4707489   23.56356812]\n"
     ]
    }
   ],
   "source": [
    "# Group estimated scores (input set) for the model\n",
    "gp_input_n = [len(Y) for Y in Y_input_raw_list]\n",
    "# SMAPE with nan values ignored (used by Kaggle)\n",
    "gp_esmape_ens = [[esmape(Y, Y_p) for Y, Y_p in zip(Y_input_raw_list, Y_input_pred_ori_list)]\n",
    "                for Y_input_pred_ori_list in Y_input_pred_ori_list_ens]\n",
    "gp_esmape = gp_esmape_ens[-1]    \n",
    "gp_esmape_mean = np.mean(gp_esmape_ens, axis=0)\n",
    "print('Input set group SMAPE:', gp_esmape_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group scores (output set) for the model\n",
    "if test_length:\n",
    "    gp_output_n = [len(Y) for Y in Y_output_raw_list]\n",
    "    # SMAPE with nan values ignored (used by Kaggle)\n",
    "    gp_smape_ens = [[smape(Y, Y_p) for Y, Y_p in zip(Y_output_raw_list, Y_output_pred_ori_list)]\n",
    "                    for Y_output_pred_ori_list in Y_output_pred_ori_list_ens]\n",
    "    gp_smape = gp_smape_ens[-1]    \n",
    "    gp_smape_mean = np.mean(gp_smape_ens, axis=0)\n",
    "    print('Output set group SMAPE:', gp_smape_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group Estimated score (input set) for Fibonacci median\n",
    "gp_esmape_fib = [esmape(Y, Y_p) for Y, Y_p in zip(Y_input_raw_list, Y_input_fib_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group score (output set) for Fibonacci median\n",
    "if test_length:\n",
    "    gp_smape_fib = [smape(Y, Y_p) for Y, Y_p in zip(Y_output_raw_list, Y_output_fib_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#### model_modified_CNN Estimated SMAPE (input set)\n",
      "|N_pages |Fibonacci| Average | Model 0 | Model 1 | Model 2 | Model 3 | Model 4 |\n",
      "|--------|---------|---------|---------|---------|---------|---------|---------|\n",
      "|  27413 |  67.261 |  70.251 |  70.304 |  70.138 |  70.179 |  70.514 |  70.119 |\n",
      "|  40041 |  47.214 |  40.666 |  40.744 |  40.528 |  40.440 |  40.729 |  40.887 |\n",
      "|  76815 |  41.531 |  29.471 |  29.143 |  29.842 |  29.265 |  29.382 |  29.722 |\n",
      "|    794 |  41.575 |  23.564 |  23.454 |  23.480 |  23.019 |  23.754 |  24.110 |\n",
      "|  Total |  47.328 |  39.249 |  39.099 |  39.394 |  39.057 |  39.262 |  39.431 |"
     ]
    }
   ],
   "source": [
    "#Estimated gp_score compare\n",
    "print('#### ' + model_name, 'Estimated SMAPE (input set)')\n",
    "print('', end='|')\n",
    "print('N_pages ', 'Fibonacci', ' Average ', sep='|', end='|')\n",
    "for run in ens_list: print(' Model', run, end=' |')\n",
    "print('')\n",
    "print('', end='|')\n",
    "print('--------', '---------', '---------', sep='|', end='|')\n",
    "for run in ens_list: print('---------', end='|')\n",
    "print('')\n",
    "for gp in gp_list:\n",
    "    print('', end='|')\n",
    "    print(' %6d ' % gp_input_n[gp], end='|')\n",
    "    print(' %7.3f ' % gp_esmape_fib[gp], end='|')\n",
    "    print(' %7.3f ' % gp_esmape_mean[gp], end='|')\n",
    "    for run in ens_list:\n",
    "        print(' %7.3f ' % gp_esmape_ens[run][gp], end='|')\n",
    "    print('')\n",
    "print('|  Total ', ' %7.3f ' % fib_esmape, ' %7.3f ' % model_esmape_ens_mean, sep='|', end='|')\n",
    "for run in ens_list:\n",
    "        print(' %7.3f ' % model_esmape_ens[run], end='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length:  # gp_score compare\n",
    "    print('#### ' + model_name, 'SMAPE (output set)')\n",
    "    print('', end='|')\n",
    "    print('N_pages ', 'Fibonacci', ' Average ', sep='|', end='|')\n",
    "    for run in ens_list: print(' Model', run, end=' |')\n",
    "    print('')\n",
    "    print('', end='|')\n",
    "    print('--------', '---------', '---------', sep='|', end='|')\n",
    "    for run in ens_list: print('---------', end='|')\n",
    "    print('')\n",
    "    for gp in gp_list:\n",
    "        print('', end='|')\n",
    "        print(' %6d ' % gp_output_n[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_fib[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_mean[gp], end='|')\n",
    "        for run in ens_list:\n",
    "            print(' %7.3f ' % gp_smape_ens[run][gp], end='|')\n",
    "        print('')\n",
    "    print('|  Total ', ' %7.3f ' % fib_smape, ' %7.3f ' % model_smape_ens_mean, sep='|', end='|')\n",
    "    for run in ens_list:\n",
    "            print(' %7.3f ' % model_smape_ens[run], end='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see Fibonacci median method do better in group 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Pre Combine\n",
    "Combine the result of the group 0 from Fibonacci median and the rest from the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select group 0 from Fibonacci median model\n",
    "fib_mask = (output_gp == 0).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_output_fib_ori = np.repeat(Y_output_fib, y_length, axis=1)\n",
    "Y_input_fib_ori = np.repeat(Y_input_fib, y_length, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The combined result for input set\n",
    "Y_input_fmc_ori_ens = []\n",
    "for Y_input_pred_ori in Y_input_pred_ori_ens:\n",
    "    Y_input_fmc_ori = fib_mask * Y_input_fib_ori + (~fib_mask) * Y_input_pred_ori\n",
    "    Y_input_fmc_ori_ens.append(Y_input_fmc_ori)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The combined result\n",
    "Y_output_fmc_ori_ens = []\n",
    "for Y_output_pred_ori in Y_output_pred_ori_ens:\n",
    "    Y_output_fmc_ori = fib_mask * Y_output_fib_ori + (~fib_mask) * Y_output_pred_ori\n",
    "    Y_output_fmc_ori_ens.append(Y_output_fmc_ori)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pre Combined Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length: \n",
    "    fmc_smape_ens = []\n",
    "    for Y_output_fmc_ori in Y_output_fmc_ori_ens:\n",
    "        fmc_smape = smape(Y_output_fmc_ori, Y_output_raw)\n",
    "        fmc_smape_ens.append(fmc_smape)        \n",
    "    fmc_smape_ens_mean = np.mean(fmc_smape_ens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fmc_esmape_ens = []\n",
    "for Y_input_fmc_ori in Y_input_fmc_ori_ens:\n",
    "    fmc_esmape = esmape(Y_input_fmc_ori, Y_input_raw)\n",
    "    fmc_esmape_ens.append(fmc_esmape)        \n",
    "fmc_esmape_ens_mean = np.mean(fmc_esmape_ens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Group combined SMAPE for input set\n",
      "38.859 (39.099) - model_modified_CNN Est 0\n",
      "39.194 (39.394) - model_modified_CNN Est 1\n",
      "38.846 (39.057) - model_modified_CNN Est 2\n",
      "38.997 (39.262) - model_modified_CNN Est 3\n",
      "39.225 (39.431) - model_modified_CNN Est 4\n",
      "39.024 (39.249) - model_modified_CNN Est Average\n"
     ]
    }
   ],
   "source": [
    "print('### Group combined SMAPE for input set')\n",
    "for run, fmc_esmape, model_esmape in zip(ens_list, fmc_esmape_ens, model_esmape_ens):\n",
    "    print('%6.3f' % fmc_esmape, '(%6.3f)' % model_esmape, '-', model_name, 'Est', run)\n",
    "print('%6.3f' % fmc_esmape_ens_mean, '(%6.3f)' % model_esmape_ens_mean, '-', model_name, 'Est', 'Average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length: \n",
    "    print('### Group combined SMAPE for output set')\n",
    "    for run, fmc_smape, model_smape in zip(ens_list, fmc_smape_ens, model_smape_ens):\n",
    "        print('%6.3f' % fmc_smape, '(%6.3f)' % model_smape, '-', model_name, run)\n",
    "    print('%6.3f' % fmc_smape_ens_mean, '(%6.3f)' % model_smape_ens_mean, '-', model_name, 'Average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Median\n",
    "Take ensemble median amount all the CNN models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_input_pred_ori_ens_med = np.median(Y_input_pred_ori_ens, axis=0)\n",
    "Y_output_pred_ori_ens_med = np.median(Y_output_pred_ori_ens, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble SMAPE for input set: 38.6974\n"
     ]
    }
   ],
   "source": [
    "ens_med_esmape = esmape(Y_input_pred_ori_ens_med, Y_input_raw)\n",
    "print('Ensemble SMAPE for input set:', ens_med_esmape)\n",
    "if test_length:\n",
    "    ens_med_smape = smape(Y_output_pred_ori_ens_med, Y_output_raw)\n",
    "    print('Ensemble SMAPE for input set:', ens_med_smape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Group Combined with Fibonacci Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_input_f_omed  = fib_mask * Y_input_fib_ori + (~fib_mask) * Y_input_pred_ori_ens_med\n",
    "Y_output_f_omed  = fib_mask * Y_output_fib_ori + (~fib_mask) * Y_output_pred_ori_ens_med"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Model Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ensemble Group Combined SMAPE for input set\n",
    "f_omed_esmape = esmape(Y_input_raw, Y_input_f_omed)\n",
    "if test_length:  # Ensemble Group Combined SMAPE for output set\n",
    "    f_omed_smape = smape(Y_output_raw, Y_output_f_omed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Ensemble Group combined SMAPE for input set\n",
      "38.526 (38.697) - model_modified_CNN Est ori_ensemble_median\n",
      "39.024 (39.249) - model_modified_CNN Est Average\n",
      "38.859 (39.099) - model_modified_CNN Est 0\n",
      "39.194 (39.394) - model_modified_CNN Est 1\n",
      "38.846 (39.057) - model_modified_CNN Est 2\n",
      "38.997 (39.262) - model_modified_CNN Est 3\n",
      "39.225 (39.431) - model_modified_CNN Est 4\n"
     ]
    }
   ],
   "source": [
    "print('### Ensemble Group combined SMAPE for input set')\n",
    "print('%6.3f' % f_omed_esmape, '(%6.3f)' % ens_med_esmape,'-', model_name, 'Est', 'ori_ensemble_median')\n",
    "print('%6.3f' % fmc_esmape_ens_mean, '(%6.3f)' % model_esmape_ens_mean, '-', model_name, 'Est', 'Average')\n",
    "for run, fmc_esmape, model_esmape in zip(ens_list, fmc_esmape_ens, model_esmape_ens):\n",
    "    print('%6.3f' % fmc_esmape, '(%6.3f)' % model_esmape, '-', model_name, 'Est', run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if test_length:\n",
    "    print('### Ensemble Group combined SMAPE for output set')\n",
    "    print('%6.3f' % f_omed_smape, '(%6.3f)' % ens_med_smape,'-', model_name, 'ori_ensemble_median')\n",
    "    print('%6.3f' % fmc_smape_ens_mean, '(%6.3f)' % model_smape_ens_mean, '-', model_name, 'Average')\n",
    "    for run, fmc_smape, model_smape in zip(ens_list, fmc_smape_ens, model_smape_ens):\n",
    "        print('%6.3f' % fmc_smape, '(%6.3f)' % model_smape, '-', model_name, run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Group Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ensemble result Y_input in each group\n",
    "Y_input_pred_ori_ens_med_list = [Y_input_pred_ori_ens_med[input_gp == gp] for gp in gp_list]\n",
    "\n",
    "# ensemble result Y_output in each group\n",
    "Y_output_pred_ori_ens_med_list = [Y_output_pred_ori_ens_med[output_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group Estimated SMAPE scores\n",
    "gp_omed_esmape = [esmape(Y, Y_p) for Y, Y_p in zip(Y_input_raw_list, Y_input_pred_ori_ens_med_list)]\n",
    "# Group SMAPE scores\n",
    "if test_length:\n",
    "    gp_omed_smape = [smape(Y, Y_p) for Y, Y_p in zip(Y_output_raw_list, Y_output_pred_ori_ens_med_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#### model_modified_CNN Estimated\n",
      "| N_pages|Fibonacci|Ens Media| Average | Model 0 | Model 1 | Model 2 | Model 3 | Model 4 |\n",
      "|--------|---------|---------|---------|---------|---------|---------|---------|---------|\n",
      "|  27413 |  67.261 |  69.984 |  70.251 |  70.304 |  70.138 |  70.179 |  70.514 |  70.119 |\n",
      "|  40041 |  47.214 |  40.085 |  40.666 |  40.744 |  40.528 |  40.440 |  40.729 |  40.887 |\n",
      "|  76815 |  41.531 |  28.860 |  29.471 |  29.143 |  29.842 |  29.265 |  29.382 |  29.722 |\n",
      "|    794 |  41.575 |  22.059 |  23.564 |  23.454 |  23.480 |  23.019 |  23.754 |  24.110 |\n",
      "|Over All|  47.328 |  38.526 |  39.249 |  39.099 |  39.394 |  39.057 |  39.262 |  39.431 |"
     ]
    }
   ],
   "source": [
    "# The Estimated gp score\n",
    "print('#### ' + model_name, 'Estimated')\n",
    "print('', end='|')\n",
    "print(' N_pages', 'Fibonacci', 'Ens Media', ' Average ', sep='|', end='|')\n",
    "for run in ens_list: print(' Model', run, end=' |')\n",
    "print('')\n",
    "print('', end='|')\n",
    "print('--------', '---------', '---------', '---------', sep='|', end='|')\n",
    "for run in ens_list: print('---------', end='|')\n",
    "print('')\n",
    "for gp in gp_list:\n",
    "    print('', end='|')\n",
    "    print(' %6d ' % gp_input_n[gp], end='|')\n",
    "    print(' %7.3f ' % gp_esmape_fib[gp], end='|')\n",
    "    print(' %7.3f ' % gp_omed_esmape[gp], end='|')\n",
    "    print(' %7.3f ' % gp_esmape_mean[gp], end='|')\n",
    "    for run in ens_list:\n",
    "        print(' %7.3f ' % gp_esmape_ens[run][gp], end='|')\n",
    "    print('')\n",
    "print('|Over All', ' %7.3f ' % fib_esmape, ' %7.3f ' % f_omed_esmape,\n",
    "      ' %7.3f ' % model_esmape_ens_mean, sep='|', end='|')\n",
    "for run in ens_list:\n",
    "        print(' %7.3f ' % model_esmape_ens[run], end='|')\n",
    "\n",
    "# Note The over all score for ensemble median (Ens Media) is the one after combining with \n",
    "# Fibonacci median model not the total score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if test_length:  # The_gp_score\n",
    "    print('#### ' + model_name)\n",
    "    print('', end='|')\n",
    "    print(' N_pages', 'Fibonacci', 'Ens Media', ' Average ', sep='|', end='|')\n",
    "    for run in ens_list: print(' Model', run, end=' |')\n",
    "    print('')\n",
    "    print('', end='|')\n",
    "    print('--------', '---------', '---------', '---------', sep='|', end='|')\n",
    "    for run in ens_list: print('---------', end='|')\n",
    "    print('')\n",
    "    for gp in gp_list:\n",
    "        print('', end='|')\n",
    "        print(' %6d ' % gp_output_n[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_fib[gp], end='|')\n",
    "        print(' %7.3f ' % gp_omed_smape[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_mean[gp], end='|')\n",
    "        for run in ens_list:\n",
    "            print(' %7.3f ' % gp_smape_ens[run][gp], end='|')\n",
    "        print('')\n",
    "    print('|Over All', ' %7.3f ' % fib_smape, ' %7.3f ' % f_omed_smape,\n",
    "          ' %7.3f ' % model_smape_ens_mean, sep='|', end='|')\n",
    "    for run in ens_list:\n",
    "            print(' %7.3f ' % model_smape_ens[run], end='|')\n",
    "\n",
    "# Note The over all score for ensemble median (Ens Media) is the one after combining with \n",
    "# Fibonacci median model not the total score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize Group Models\n",
    "Pages in one group could be predicted better by the models trained in other groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group Models in other Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "if test_length == 0:\n",
    "    Y_input_all_ori_list_ens = unlog(np.array([[untransform(model.predict([X_input, A_input], batch_size=4096*2), \n",
    "                                                            Y_input_center, input_scale) \n",
    "                                                for model in models] for models in models_ens]))\n",
    "#Takes 4 min on PC and NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Y_output_all_ori_list_ens = unlog(np.array([[untransform(model.predict([X_output, A_output], batch_size=4096*2), \n",
    "                                                         Y_output_center, output_scale) \n",
    "                                             for model in models] for models in models_ens]))\n",
    "#Takes 4 min on PC and NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take Ensemble median\n",
    "Y_output_all_ori_list_ens_med = np.median(Y_output_all_ori_list_ens, axis=0)\n",
    "if test_length == 0:\n",
    "    Y_input_all_ori_list_ens_med = np.median(Y_input_all_ori_list_ens, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Group SMAPE for each group models (input set)\n",
    "#row index: group model; column index: group of Y\n",
    "if test_length == 0:\n",
    "    esmape_gpm_gp_ens_med = [[esmape(Y, Y_p[input_gp == gp]) for gp, Y in zip(gp_list, Y_input_raw_list)] \n",
    "                            for Y_p in Y_input_all_ori_list_ens_med]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Group SMAPE for each group models (output set)\n",
    "#row index: group model; column index: group of Y\n",
    "if test_length:\n",
    "    smape_gpm_gp_ens_med = [[smape(Y, Y_p[output_gp == gp]) for gp, Y in zip(gp_list, Y_output_raw_list)] \n",
    "                            for Y_p in Y_output_all_ori_list_ens_med]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ens. Median   | group 0| group 1| group 2| group 3|\n",
      "--------------|--------|--------|--------|--------|\n",
      "group model 0 |  69.98 |  47.23 |  41.56 |  41.67 |\n",
      "group model 1 |  69.90 |  40.08 |  36.06 |  40.77 |\n",
      "group model 2 |  73.06 |  44.14 |  28.86 |  29.83 |\n",
      "group model 3 |  79.95 |  52.05 |  39.49 |  22.06 |\n"
     ]
    }
   ],
   "source": [
    "if test_length:\n",
    "    print('Ens. Median   ', end='|')\n",
    "    for j in range(len(smape_gpm_gp_ens_med[0])): print(' group', j, end='|')\n",
    "    print('')\n",
    "    print('--------------', end='|')\n",
    "    for j in range(len(smape_gpm_gp_ens_med[0])): print('--------', end='|')\n",
    "    print('')\n",
    "    for i in range(len(smape_gpm_gp_ens_med)):\n",
    "        print('group model', i, end=' |')\n",
    "        for j in range(len(smape_gpm_gp_ens_med[0])):\n",
    "            print(' %6.2f ' % smape_gpm_gp_ens_med[i][j], end='|')\n",
    "        print('')\n",
    "else:\n",
    "    print('Ens. Median   ', end='|')\n",
    "    for j in range(len(esmape_gpm_gp_ens_med[0])): print(' group', j, end='|')\n",
    "    print('')\n",
    "    print('--------------', end='|')\n",
    "    for j in range(len(esmape_gpm_gp_ens_med[0])): print('--------', end='|')\n",
    "    print('')\n",
    "    for i in range(len(esmape_gpm_gp_ens_med)):\n",
    "        print('group model', i, end=' |')\n",
    "        for j in range(len(esmape_gpm_gp_ens_med[0])):\n",
    "            print(' %6.2f ' % esmape_gpm_gp_ens_med[i][j], end='|')\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In validation mode, group 3 do better in group model 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  69.98 |  40.08 |  28.86 |  22.06 |"
     ]
    }
   ],
   "source": [
    "if test_length: \n",
    "    for x in gp_omed_smape: print(' %6.2f ' % x, end='|')\n",
    "else: \n",
    "    for x in gp_omed_esmape: print(' %6.2f ' % x, end='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  67.26 |  47.21 |  41.53 |  41.57 |"
     ]
    }
   ],
   "source": [
    "if test_length: \n",
    "    for x in gp_smape_fib: print(' %6.2f ' % x, end='|')\n",
    "else: \n",
    "    for x in gp_esmape_fib: print(' %6.2f ' % x, end='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimized Group Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized group model list = [0,1,2,2,]\n"
     ]
    }
   ],
   "source": [
    "if test_length:\n",
    "    gpm_list = np.argmin(smape_gpm_gp_ens_med,axis=0)\n",
    "else:\n",
    "    gpm_list = np.array([0,1,2,2])  # Use group model 2 for group 3\n",
    "\n",
    "print('Optimized group model list = [', end='')\n",
    "for x in gpm_list: print(x, end=',')\n",
    "print(']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpm_mask = np.array([gpm_list == gp for gp in gp_list]).T\n",
    "Y_output_ori_list_ens_med_temp = np.tensordot(gpm_mask, Y_output_all_ori_list_ens_med, axes=([1],[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "gp_mask = np.array([output_gp == gp for gp in gp_list])\n",
    "gp_mask = gp_mask.reshape(len(gp_mask), len(gp_mask[0]), 1)\n",
    "Y_output_ori_list_ens_med_temp_mask = gp_mask * Y_output_ori_list_ens_med_temp\n",
    "Y_output_gpm_ens_med = np.sum(Y_output_ori_list_ens_med_temp_mask, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del Y_output_ori_list_ens_med_temp\n",
    "del Y_output_ori_list_ens_med_temp_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length: \n",
    "    gpm_ens_smape = smape(Y_output_raw, Y_output_gpm_ens_med)\n",
    "    print(gpm_ens_smape, ens_med_smape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length: Y_output_gpm_ens_med_list = [Y_output_gpm_ens_med[output_gp == gp] for gp in gp_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group SMAPE scores\n",
    "if test_length:\n",
    "    gp_gpm_ens_med_smape = [smape(Y, Y_p) for Y, Y_p in zip(Y_output_raw_list, Y_output_gpm_ens_med_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length:\n",
    "    #print('#### ' + model_name + 'group model optimized')\n",
    "    #print('', end='|')\n",
    "    print(' N_pages', 'Fibonacci', 'GPM Optim', 'Ens Media', ' Average ', sep='|', end='|')\n",
    "    #or run in ens_list: print(' Model', run, end=' |')\n",
    "    print('')\n",
    "    print('', end='|')\n",
    "    print('--------', '---------', '---------', '---------', '---------', sep='|', end='|')\n",
    "    #for run in ens_list: print('---------', end='|')\n",
    "    print('')\n",
    "    for gp in gp_list:\n",
    "        print('', end='|')\n",
    "        print(' %6d ' % gp_output_n[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_fib[gp], end='|')\n",
    "        print(' %7.3f ' % gp_gpm_ens_med_smape[gp], end='|')\n",
    "        print(' %7.3f ' % gp_omed_smape[gp], end='|')\n",
    "        print(' %7.3f ' % gp_smape_mean[gp], end='|')\n",
    "        print('')\n",
    "    #print('|Over All', ' %7.3f ' % fib_smape, ' %7.3f ' % f_omed_smape,\n",
    "    #      ' %7.3f ' % model_smape_ens_mean, sep='|', end='|')    \n",
    "# Note The over all score for ensemble median (Ens Media) is the one after combining with \n",
    "# Fibonacci median model not the total score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combined with Fibonacci Median Result\n",
    "Our final output prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Our final output prediction\n",
    "Y_output_f_gpm = fib_mask * Y_output_fib_ori + (~fib_mask) * Y_output_gpm_ens_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if test_length:  # SMAPE for final output set\n",
    "    f_gpm_ens_smape = smape(Y_output_raw, Y_output_f_gpm)\n",
    "    print('%6.3f' % f_gpm_ens_smape, '(%6.3f)' % gpm_ens_smape, '-', model_name, 'GPM Optim Ens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Free some memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87356"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del Y_input_log\n",
    "del Y_input_raw_log\n",
    "del Y_input_pred\n",
    "del Y_input_pred_ori\n",
    "del Y_input_pred_ori_ens\n",
    "del Y_input_pred_ori_ens_med\n",
    "del Y_input_fib_ori\n",
    "del Y_input_fmc_ori\n",
    "del Y_input_f_omed\n",
    "del Y_input_all_ori_list_ens_med\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if test_length == 0: del Y_input_all_ori_list_ens\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                        Type       Data/Info\n",
      "----------------------------------------------------\n",
      "A                               ndarray    675x2: 1350 elems, type `float32`, 5400 bytes\n",
      "A_in                            ndarray    794x2: 1588 elems, type `float32`, 6352 bytes\n",
      "A_in_sh                         ndarray    794x2: 1588 elems, type `float32`, 6352 bytes\n",
      "A_input                         ndarray    145063x2: 290126 elems, type `float32`, 1160504 bytes (1.1067428588867188 Mb)\n",
      "A_output                        ndarray    145063x2: 290126 elems, type `float32`, 1160504 bytes (1.1067428588867188 Mb)\n",
      "X                               ndarray    675x64: 43200 elems, type `float32`, 172800 bytes (168.75 kb)\n",
      "X_in                            ndarray    794x64: 50816 elems, type `float32`, 203264 bytes (198.5 kb)\n",
      "X_in_sh                         ndarray    794x64: 50816 elems, type `float32`, 203264 bytes (198.5 kb)\n",
      "X_input                         ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_center                  ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "X_input_log                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_num                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_ori                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_raw                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_raw_log                 ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_input_scale                   ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "X_output                        ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_center                 ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "X_output_log                    ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_num                    ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_ori                    ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_raw                    ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_raw_log                ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "X_output_scale                  ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_in                            ndarray    794x64: 50816 elems, type `float32`, 203264 bytes (198.5 kb)\n",
      "Y_in_sh                         ndarray    794x64: 50816 elems, type `float32`, 203264 bytes (198.5 kb)\n",
      "Y_input                         ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_input_center                  ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_input_fib                     ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_input_num                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_input_ori                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_input_raw                     ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_input_scale                   ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_output_all_ori_list_ens       ndarray    5x4x145063x64: 185680640 elems, type `float32`, 742722560 bytes (708.3154296875 Mb)\n",
      "Y_output_all_ori_list_ens_med   ndarray    4x145063x64: 37136128 elems, type `float32`, 148544512 bytes (141.6630859375 Mb)\n",
      "Y_output_center                 ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_output_f_gpm                  ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_f_omed                 ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_fib                    ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "Y_output_fib_ori                ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_fmc_ori                ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_gpm_ens_med            ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_pred                   ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_pred_ori               ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_pred_ori_ens           ndarray    5x145063x64: 46420160 elems, type `float32`, 185680640 bytes (177.078857421875 Mb)\n",
      "Y_output_pred_ori_ens_med       ndarray    145063x64: 9284032 elems, type `float32`, 37136128 bytes (35.415771484375 Mb)\n",
      "Y_output_scale                  ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "fib_input_median                ndarray    145063: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "fib_mask                        ndarray    145063x1: 145063 elems, type `bool`, 145063 bytes (141.6630859375 kb)\n",
      "fib_output_median               ndarray    145063: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "gp_esmape_mean                  ndarray    4: 4 elems, type `float32`, 16 bytes\n",
      "gp_mask                         ndarray    4x145063x1: 580252 elems, type `bool`, 580252 bytes (566.65234375 kb)\n",
      "gpm_list                        ndarray    4: 4 elems, type `int32`, 16 bytes\n",
      "gpm_mask                        ndarray    4x4: 16 elems, type `bool`, 16 bytes\n",
      "input_center                    ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "input_gp                        ndarray    145063: 145063 elems, type `int32`, 580252 bytes (566.65234375 kb)\n",
      "input_index_list_comb           ndarray    145063: 145063 elems, type `int32`, 580252 bytes (566.65234375 kb)\n",
      "input_index_range               ndarray    145063: 145063 elems, type `int32`, 580252 bytes (566.65234375 kb)\n",
      "input_scale                     ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "o                               ndarray    794: 794 elems, type `int32`, 3176 bytes\n",
      "output_center                   ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "output_df_dates_all             ndarray    100: 100 elems, type `datetime64[D]`, 800 bytes\n",
      "output_gp                       ndarray    145063: 145063 elems, type `int32`, 580252 bytes (566.65234375 kb)\n",
      "output_index_list_comb          ndarray    145063: 145063 elems, type `int32`, 580252 bytes (566.65234375 kb)\n",
      "output_scale                    ndarray    145063x1: 145063 elems, type `float32`, 580252 bytes (566.65234375 kb)\n",
      "shuffle_index                   ndarray    794: 794 elems, type `int32`, 3176 bytes\n",
      "y                               ndarray    64: 64 elems, type `float32`, 256 bytes\n"
     ]
    }
   ],
   "source": [
    "%whos ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output Prediction\n",
    "For predicting stage only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15',\n",
       "       '2017-09-16', '2017-09-17', '2017-09-18', '2017-09-19', '2017-09-20',\n",
       "       '2017-09-21', '2017-09-22', '2017-09-23', '2017-09-24', '2017-09-25',\n",
       "       '2017-09-26', '2017-09-27', '2017-09-28', '2017-09-29', '2017-09-30',\n",
       "       '2017-10-01', '2017-10-02', '2017-10-03', '2017-10-04', '2017-10-05',\n",
       "       '2017-10-06', '2017-10-07', '2017-10-08', '2017-10-09', '2017-10-10',\n",
       "       '2017-10-11', '2017-10-12', '2017-10-13', '2017-10-14', '2017-10-15',\n",
       "       '2017-10-16', '2017-10-17', '2017-10-18', '2017-10-19', '2017-10-20',\n",
       "       '2017-10-21', '2017-10-22', '2017-10-23', '2017-10-24', '2017-10-25',\n",
       "       '2017-10-26', '2017-10-27', '2017-10-28', '2017-10-29', '2017-10-30',\n",
       "       '2017-10-31', '2017-11-01', '2017-11-02', '2017-11-03', '2017-11-04',\n",
       "       '2017-11-05', '2017-11-06', '2017-11-07', '2017-11-08', '2017-11-09',\n",
       "       '2017-11-10', '2017-11-11', '2017-11-12', '2017-11-13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_output_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if test_length == 0:\n",
    "    result_df = pd.DataFrame(Y_output_f_gpm, columns = Y_output_dates)  # using ensemble median combined result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_df['Page'] = input_df['Page']  # Append 'Page' column from input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_df = pd.melt(result_df, id_vars='Page', var_name='date',\n",
    "                    value_name='Visits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output DataFrame\n",
    "Require additional 3 GB of memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'key_2.csv'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%%% Reading data  key_2.csv ...done!\n"
     ]
    }
   ],
   "source": [
    "if test_length == 0:\n",
    "    print('%%% Reading data ', key_file, '...', end = '', flush = True)\n",
    "    output_df = pd.read_csv('../data/' + key_file)\n",
    "    print('done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Peak memory usage: additional 2 GB \n",
    "output_df['date'] = output_df['Page'].apply(lambda a: a[-10:])  # take the last 10 characters from 'Page' as date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "output_df['Page'] = output_df['Page'].apply(lambda a: a[:-11])  # remove the last 10 caharacters from 'Page'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8993906 entries, 0 to 8993905\n",
      "Data columns (total 3 columns):\n",
      "Page    object\n",
      "Id      object\n",
      "date    object\n",
      "dtypes: object(3)\n",
      "memory usage: 205.9+ MB\n"
     ]
    }
   ],
   "source": [
    "output_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2017-09-13', '2017-09-14', '2017-09-15', '2017-09-16',\n",
       "       '2017-09-17', '2017-09-18', '2017-09-19', '2017-09-20',\n",
       "       '2017-09-21', '2017-09-22', '2017-09-23', '2017-09-24',\n",
       "       '2017-09-25', '2017-09-26', '2017-09-27', '2017-09-28',\n",
       "       '2017-09-29', '2017-09-30', '2017-10-01', '2017-10-02',\n",
       "       '2017-10-03', '2017-10-04', '2017-10-05', '2017-10-06',\n",
       "       '2017-10-07', '2017-10-08', '2017-10-09', '2017-10-10',\n",
       "       '2017-10-11', '2017-10-12', '2017-10-13', '2017-10-14',\n",
       "       '2017-10-15', '2017-10-16', '2017-10-17', '2017-10-18',\n",
       "       '2017-10-19', '2017-10-20', '2017-10-21', '2017-10-22',\n",
       "       '2017-10-23', '2017-10-24', '2017-10-25', '2017-10-26',\n",
       "       '2017-10-27', '2017-10-28', '2017-10-29', '2017-10-30',\n",
       "       '2017-10-31', '2017-11-01', '2017-11-02', '2017-11-03',\n",
       "       '2017-11-04', '2017-11-05', '2017-11-06', '2017-11-07',\n",
       "       '2017-11-08', '2017-11-09', '2017-11-10', '2017-11-11',\n",
       "       '2017-11-12', '2017-11-13', '2017-09-13', '2017-09-14', '2017-09-15'], dtype=object)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_df['date'].values[0:y_length+1]  \n",
    "# Make sure the range is less than y_length days (see if the dates resume after y_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15',\n",
       "       '2017-09-16', '2017-09-17', '2017-09-18', '2017-09-19', '2017-09-20',\n",
       "       '2017-09-21', '2017-09-22', '2017-09-23', '2017-09-24', '2017-09-25',\n",
       "       '2017-09-26', '2017-09-27', '2017-09-28', '2017-09-29', '2017-09-30',\n",
       "       '2017-10-01', '2017-10-02', '2017-10-03', '2017-10-04', '2017-10-05',\n",
       "       '2017-10-06', '2017-10-07', '2017-10-08', '2017-10-09', '2017-10-10',\n",
       "       '2017-10-11', '2017-10-12', '2017-10-13', '2017-10-14', '2017-10-15',\n",
       "       '2017-10-16', '2017-10-17', '2017-10-18', '2017-10-19', '2017-10-20',\n",
       "       '2017-10-21', '2017-10-22', '2017-10-23', '2017-10-24', '2017-10-25',\n",
       "       '2017-10-26', '2017-10-27', '2017-10-28', '2017-10-29', '2017-10-30',\n",
       "       '2017-10-31', '2017-11-01', '2017-11-02', '2017-11-03', '2017-11-04',\n",
       "       '2017-11-05', '2017-11-06', '2017-11-07', '2017-11-08', '2017-11-09',\n",
       "       '2017-11-10', '2017-11-11', '2017-11-12', '2017-11-13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_output_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Page</th>\n",
       "      <th>Id</th>\n",
       "      <th>date</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>0b293039387a</td>\n",
       "      <td>2017-09-13</td>\n",
       "      <td>443.147980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>7114389dd824</td>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>437.231018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>057b02ff1f09</td>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>440.334625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>bd2aca21caa3</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>490.773438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>c0effb42cdd5</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>511.724609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>4ccd369adefc</td>\n",
       "      <td>2017-09-18</td>\n",
       "      <td>476.478058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>67d5c2ebc330</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>442.491364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>1c51606e416b</td>\n",
       "      <td>2017-09-20</td>\n",
       "      <td>424.396545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>0f27b91481db</td>\n",
       "      <td>2017-09-21</td>\n",
       "      <td>451.346588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>07f5f08a49fa</td>\n",
       "      <td>2017-09-22</td>\n",
       "      <td>429.085632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>de5e0bd4769d</td>\n",
       "      <td>2017-09-23</td>\n",
       "      <td>481.419067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>4992950a2651</td>\n",
       "      <td>2017-09-24</td>\n",
       "      <td>485.398956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>6d53bd4dc888</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>422.585602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>1bbd6403124c</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>424.643707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>e8e7ff40facc</td>\n",
       "      <td>2017-09-27</td>\n",
       "      <td>423.142670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>8a4b1c6af656</td>\n",
       "      <td>2017-09-28</td>\n",
       "      <td>409.952789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>1e24475ddb8c</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>408.687073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>02e034fdd2c4</td>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>463.722443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>1cc7eb149dbd</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>471.852844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>fb518bfef5ab</td>\n",
       "      <td>2017-10-02</td>\n",
       "      <td>418.631012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>1d1f1519ac13</td>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>409.591309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>ac35a6c2b36c</td>\n",
       "      <td>2017-10-04</td>\n",
       "      <td>410.134460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>5ff1782a4045</td>\n",
       "      <td>2017-10-05</td>\n",
       "      <td>403.017334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>148aa06a6a65</td>\n",
       "      <td>2017-10-06</td>\n",
       "      <td>406.245331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>b94a0af77529</td>\n",
       "      <td>2017-10-07</td>\n",
       "      <td>466.655884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>0a214bdcec3d</td>\n",
       "      <td>2017-10-08</td>\n",
       "      <td>476.450775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>6562a45df4fd</td>\n",
       "      <td>2017-10-09</td>\n",
       "      <td>451.397003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>220e7ae73a6d</td>\n",
       "      <td>2017-10-10</td>\n",
       "      <td>409.439636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>435af699b0f2</td>\n",
       "      <td>2017-10-11</td>\n",
       "      <td>402.936615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>007_スペクター_ja.wikipedia.org_all-access_all-agents</td>\n",
       "      <td>a2c44639e9e4</td>\n",
       "      <td>2017-10-12</td>\n",
       "      <td>397.637421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993876</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f4f823ef5696</td>\n",
       "      <td>2017-10-15</td>\n",
       "      <td>264.610870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993877</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f0320830b44c</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>242.583679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993878</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>342e1d97fd05</td>\n",
       "      <td>2017-10-17</td>\n",
       "      <td>237.217606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993879</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>b1477f44cd9b</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>235.276077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993880</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>da253f622bc2</td>\n",
       "      <td>2017-10-19</td>\n",
       "      <td>235.090286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993881</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>2011edc98ab6</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>240.775009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993882</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>6d43be567135</td>\n",
       "      <td>2017-10-21</td>\n",
       "      <td>257.835693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993883</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>63fba276ae93</td>\n",
       "      <td>2017-10-22</td>\n",
       "      <td>268.598633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993884</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>4d52c4f26bea</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>243.989395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993885</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>4fdb762699cc</td>\n",
       "      <td>2017-10-24</td>\n",
       "      <td>238.763672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993886</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>aeb4cd159568</td>\n",
       "      <td>2017-10-25</td>\n",
       "      <td>237.007935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993887</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>61f9ad4a134b</td>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>235.847244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993888</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>65a1df224420</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>238.950134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993889</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f218e2a344f0</td>\n",
       "      <td>2017-10-28</td>\n",
       "      <td>253.694290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993890</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>55a0f4f60bcf</td>\n",
       "      <td>2017-10-29</td>\n",
       "      <td>266.345001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993891</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f1f430469c68</td>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>244.689896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993892</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f560ef9fa473</td>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>249.027679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993893</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>ec2d0d713c50</td>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>239.717880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993894</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>4f13b0cd46f9</td>\n",
       "      <td>2017-11-02</td>\n",
       "      <td>240.499878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993895</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>3464386275bc</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>245.448807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993896</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>2b1ae259f55a</td>\n",
       "      <td>2017-11-04</td>\n",
       "      <td>265.262573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993897</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>75deaba16778</td>\n",
       "      <td>2017-11-05</td>\n",
       "      <td>279.702637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993898</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>a4d97ae6a195</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>249.311417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993899</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>4e0c1e64f738</td>\n",
       "      <td>2017-11-07</td>\n",
       "      <td>242.225159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993900</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>9a735df742e2</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>229.695694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993901</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>f78fbaa305ad</td>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>236.239456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993902</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>33aecaf259d1</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>245.724426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993903</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>a36228b64466</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>265.909607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993904</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>26887d3e5c99</td>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>279.667480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993905</th>\n",
       "      <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
       "      <td>06ad06979f80</td>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>251.942368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8993906 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Page            Id  \\\n",
       "0        007_スペクター_ja.wikipedia.org_all-access_all-agents  0b293039387a   \n",
       "1        007_スペクター_ja.wikipedia.org_all-access_all-agents  7114389dd824   \n",
       "2        007_スペクター_ja.wikipedia.org_all-access_all-agents  057b02ff1f09   \n",
       "3        007_スペクター_ja.wikipedia.org_all-access_all-agents  bd2aca21caa3   \n",
       "4        007_スペクター_ja.wikipedia.org_all-access_all-agents  c0effb42cdd5   \n",
       "5        007_スペクター_ja.wikipedia.org_all-access_all-agents  4ccd369adefc   \n",
       "6        007_スペクター_ja.wikipedia.org_all-access_all-agents  67d5c2ebc330   \n",
       "7        007_スペクター_ja.wikipedia.org_all-access_all-agents  1c51606e416b   \n",
       "8        007_スペクター_ja.wikipedia.org_all-access_all-agents  0f27b91481db   \n",
       "9        007_スペクター_ja.wikipedia.org_all-access_all-agents  07f5f08a49fa   \n",
       "10       007_スペクター_ja.wikipedia.org_all-access_all-agents  de5e0bd4769d   \n",
       "11       007_スペクター_ja.wikipedia.org_all-access_all-agents  4992950a2651   \n",
       "12       007_スペクター_ja.wikipedia.org_all-access_all-agents  6d53bd4dc888   \n",
       "13       007_スペクター_ja.wikipedia.org_all-access_all-agents  1bbd6403124c   \n",
       "14       007_スペクター_ja.wikipedia.org_all-access_all-agents  e8e7ff40facc   \n",
       "15       007_スペクター_ja.wikipedia.org_all-access_all-agents  8a4b1c6af656   \n",
       "16       007_スペクター_ja.wikipedia.org_all-access_all-agents  1e24475ddb8c   \n",
       "17       007_スペクター_ja.wikipedia.org_all-access_all-agents  02e034fdd2c4   \n",
       "18       007_スペクター_ja.wikipedia.org_all-access_all-agents  1cc7eb149dbd   \n",
       "19       007_スペクター_ja.wikipedia.org_all-access_all-agents  fb518bfef5ab   \n",
       "20       007_スペクター_ja.wikipedia.org_all-access_all-agents  1d1f1519ac13   \n",
       "21       007_スペクター_ja.wikipedia.org_all-access_all-agents  ac35a6c2b36c   \n",
       "22       007_スペクター_ja.wikipedia.org_all-access_all-agents  5ff1782a4045   \n",
       "23       007_スペクター_ja.wikipedia.org_all-access_all-agents  148aa06a6a65   \n",
       "24       007_スペクター_ja.wikipedia.org_all-access_all-agents  b94a0af77529   \n",
       "25       007_スペクター_ja.wikipedia.org_all-access_all-agents  0a214bdcec3d   \n",
       "26       007_スペクター_ja.wikipedia.org_all-access_all-agents  6562a45df4fd   \n",
       "27       007_スペクター_ja.wikipedia.org_all-access_all-agents  220e7ae73a6d   \n",
       "28       007_スペクター_ja.wikipedia.org_all-access_all-agents  435af699b0f2   \n",
       "29       007_スペクター_ja.wikipedia.org_all-access_all-agents  a2c44639e9e4   \n",
       "...                                                   ...           ...   \n",
       "8993876       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f4f823ef5696   \n",
       "8993877       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f0320830b44c   \n",
       "8993878       龙生九子_zh.wikipedia.org_mobile-web_all-agents  342e1d97fd05   \n",
       "8993879       龙生九子_zh.wikipedia.org_mobile-web_all-agents  b1477f44cd9b   \n",
       "8993880       龙生九子_zh.wikipedia.org_mobile-web_all-agents  da253f622bc2   \n",
       "8993881       龙生九子_zh.wikipedia.org_mobile-web_all-agents  2011edc98ab6   \n",
       "8993882       龙生九子_zh.wikipedia.org_mobile-web_all-agents  6d43be567135   \n",
       "8993883       龙生九子_zh.wikipedia.org_mobile-web_all-agents  63fba276ae93   \n",
       "8993884       龙生九子_zh.wikipedia.org_mobile-web_all-agents  4d52c4f26bea   \n",
       "8993885       龙生九子_zh.wikipedia.org_mobile-web_all-agents  4fdb762699cc   \n",
       "8993886       龙生九子_zh.wikipedia.org_mobile-web_all-agents  aeb4cd159568   \n",
       "8993887       龙生九子_zh.wikipedia.org_mobile-web_all-agents  61f9ad4a134b   \n",
       "8993888       龙生九子_zh.wikipedia.org_mobile-web_all-agents  65a1df224420   \n",
       "8993889       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f218e2a344f0   \n",
       "8993890       龙生九子_zh.wikipedia.org_mobile-web_all-agents  55a0f4f60bcf   \n",
       "8993891       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f1f430469c68   \n",
       "8993892       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f560ef9fa473   \n",
       "8993893       龙生九子_zh.wikipedia.org_mobile-web_all-agents  ec2d0d713c50   \n",
       "8993894       龙生九子_zh.wikipedia.org_mobile-web_all-agents  4f13b0cd46f9   \n",
       "8993895       龙生九子_zh.wikipedia.org_mobile-web_all-agents  3464386275bc   \n",
       "8993896       龙生九子_zh.wikipedia.org_mobile-web_all-agents  2b1ae259f55a   \n",
       "8993897       龙生九子_zh.wikipedia.org_mobile-web_all-agents  75deaba16778   \n",
       "8993898       龙生九子_zh.wikipedia.org_mobile-web_all-agents  a4d97ae6a195   \n",
       "8993899       龙生九子_zh.wikipedia.org_mobile-web_all-agents  4e0c1e64f738   \n",
       "8993900       龙生九子_zh.wikipedia.org_mobile-web_all-agents  9a735df742e2   \n",
       "8993901       龙生九子_zh.wikipedia.org_mobile-web_all-agents  f78fbaa305ad   \n",
       "8993902       龙生九子_zh.wikipedia.org_mobile-web_all-agents  33aecaf259d1   \n",
       "8993903       龙生九子_zh.wikipedia.org_mobile-web_all-agents  a36228b64466   \n",
       "8993904       龙生九子_zh.wikipedia.org_mobile-web_all-agents  26887d3e5c99   \n",
       "8993905       龙生九子_zh.wikipedia.org_mobile-web_all-agents  06ad06979f80   \n",
       "\n",
       "               date      Visits  \n",
       "0        2017-09-13  443.147980  \n",
       "1        2017-09-14  437.231018  \n",
       "2        2017-09-15  440.334625  \n",
       "3        2017-09-16  490.773438  \n",
       "4        2017-09-17  511.724609  \n",
       "5        2017-09-18  476.478058  \n",
       "6        2017-09-19  442.491364  \n",
       "7        2017-09-20  424.396545  \n",
       "8        2017-09-21  451.346588  \n",
       "9        2017-09-22  429.085632  \n",
       "10       2017-09-23  481.419067  \n",
       "11       2017-09-24  485.398956  \n",
       "12       2017-09-25  422.585602  \n",
       "13       2017-09-26  424.643707  \n",
       "14       2017-09-27  423.142670  \n",
       "15       2017-09-28  409.952789  \n",
       "16       2017-09-29  408.687073  \n",
       "17       2017-09-30  463.722443  \n",
       "18       2017-10-01  471.852844  \n",
       "19       2017-10-02  418.631012  \n",
       "20       2017-10-03  409.591309  \n",
       "21       2017-10-04  410.134460  \n",
       "22       2017-10-05  403.017334  \n",
       "23       2017-10-06  406.245331  \n",
       "24       2017-10-07  466.655884  \n",
       "25       2017-10-08  476.450775  \n",
       "26       2017-10-09  451.397003  \n",
       "27       2017-10-10  409.439636  \n",
       "28       2017-10-11  402.936615  \n",
       "29       2017-10-12  397.637421  \n",
       "...             ...         ...  \n",
       "8993876  2017-10-15  264.610870  \n",
       "8993877  2017-10-16  242.583679  \n",
       "8993878  2017-10-17  237.217606  \n",
       "8993879  2017-10-18  235.276077  \n",
       "8993880  2017-10-19  235.090286  \n",
       "8993881  2017-10-20  240.775009  \n",
       "8993882  2017-10-21  257.835693  \n",
       "8993883  2017-10-22  268.598633  \n",
       "8993884  2017-10-23  243.989395  \n",
       "8993885  2017-10-24  238.763672  \n",
       "8993886  2017-10-25  237.007935  \n",
       "8993887  2017-10-26  235.847244  \n",
       "8993888  2017-10-27  238.950134  \n",
       "8993889  2017-10-28  253.694290  \n",
       "8993890  2017-10-29  266.345001  \n",
       "8993891  2017-10-30  244.689896  \n",
       "8993892  2017-10-31  249.027679  \n",
       "8993893  2017-11-01  239.717880  \n",
       "8993894  2017-11-02  240.499878  \n",
       "8993895  2017-11-03  245.448807  \n",
       "8993896  2017-11-04  265.262573  \n",
       "8993897  2017-11-05  279.702637  \n",
       "8993898  2017-11-06  249.311417  \n",
       "8993899  2017-11-07  242.225159  \n",
       "8993900  2017-11-08  229.695694  \n",
       "8993901  2017-11-09  236.239456  \n",
       "8993902  2017-11-10  245.724426  \n",
       "8993903  2017-11-11  265.909607  \n",
       "8993904  2017-11-12  279.667480  \n",
       "8993905  2017-11-13  251.942368  \n",
       "\n",
       "[8993906 rows x 4 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_df = output_df.merge(result_df, how='left')  # fill the 'Visits\" from result\n",
    "output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del result_df\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Visits, dtype: float32)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there is any null value\n",
    "output_df.loc[output_df.Visits.isnull(), 'Visits']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#output_df.loc[output_df.Visits.isnull(), 'Visits'] = 0.0  # Uncommend this line to Replace NaN with 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/submit_2_model_modified_CNN.csv'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_file_path = '../results/submit_2_' + model_name + '.csv'; output_file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%%% Writing result to ../results/submit_2_model_modified_CNN.csv ...done!\n"
     ]
    }
   ],
   "source": [
    "print('%%% Writing result to ' + output_file_path + ' ...', \n",
    "      end = '', flush = True)\n",
    "output_df[['Id','Visits']].to_csv(output_file_path, index = False, float_format='%.3f')\n",
    "print('done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaggle score (SMAPE): 37.75808"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
